Disambiguate Gripper State in Grasp-Based Tasks: Pseudo-Tactile as Feedback Enables Pure Simulation Learning

arXiv:2503.23835 · cs.RO · Submitted 2025-03-31 · Read on arXiv

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Introduction to the show: ident: Robotics Radio. Generated commentary on the latest robotics and control papers.

Rosa: Today's paper: "Disambiguate Gripper State in Grasp-Based Tasks".

Dev: Grasp-based manipulation tasks are fundamental to robotics, but ambiguity in gripper state significantly reduces the robustness of imitation learning policies.

Rosa: First, who's behind it and why it matters.

Title and authors: Rosa: So we're diving into this paper today, "Disambiguate Gripper State in Grasp-Based Tasks: Pseudo-Tactile as Feedback Enables Pure Simulation Learning." It sounds like they're tackling a big problem where robots struggle to know if they actually have an object in their gripper when doing manipulation tasks.

Dev: Right, Rosa. I'm interested in how this works because from my end, the loop rate and any latency issues are always a concern when we talk about real-time control systems like this. I wonder if the pseudo-tactile feedback they propose will introduce too much computational overhead for our typical hardware setup.

Taro: From an autonomy standpoint, I'm curious about what happens when the world misbehaves and that state ambiguity kicks in; how does this method help the AI recover from errors?

Rosa: Well, the abstract tells us they tackle this by using pseudo-tactile feedback to give a better idea of whether a grasp is successful, which then lets them use clean binary observations for simulation training. It seems like they’re trying to solve that data collection bottleneck we always face in real-world robotics.

Dev: That's the core idea, right? They identified that because human demonstrations rely on tactile feedback to know success, but the policy only sees a feedforward binary state, it gets confused when things go wrong during operation.

Taro: So if the policy can distinguish between a successful grasp and an empty one without needing actual expensive tactile hardware or constant data collection, that opens up a lot of possibilities for developing more robust autonomy.

Rosa: Exactly. They propose using the force-controlled gripper itself as a sensor, interpreting the joint angle deformation as pseudo-tactile information to override the standard binary state observation when necessary.

Dev: I'm thinking about the mechanism there; they're essentially designing a closed-loop controller that forces an open state if it senses no object has been grasped, which sounds like it handles those premature closures we see in practice.

Taro: That sounds like a direct way to prevent incorrect actions, like pulling when something isn't actually held, which is crucial for reliable autonomous operation in unpredictable environments.

Title and authors: Rosa: They show that this approach allows the policy to use a noise-free binary gripper state observation, which is what lets them leverage the simulation environment much more effectively. This moves them away from needing messy real-world data for training policies.

Dev: That's interesting because it directly addresses the sim-to-real gap by letting the policy learn in simulation with cleaner inputs, and they mention this helps avoid gripper disturbances during data collection as well.

Taro: If you can train a policy purely in simulation using these refined observations, you bypass a lot of the issues associated with collecting massive amounts of real-world teleoperation data for every little tweak.

Rosa: They validate this by testing it on three real-world tasks—pick-and-lift, drawer-opening, and oven-opening—and their results show that the policy trained this way outperforms baselines trained with real-world teleoperation data across all metrics.

Dev: I need to know more about the practical application outside of controlled lab settings; Rosa, does this method hold up when we introduce unexpected physical disturbances during actual deployment?

Rosa: That’s a big question for me, Dev; they report a "one hundred percent Disturbance Resilience Success Rate" across those tasks, which suggests it handles things well under real-world stress. However, they also acknowledge that the method relies on using admittance control when transferring to the physical world to handle kinematic discrepancies with articulated objects like oven doors.

Taro: So even with that safety net of admittance control for real-world transfer, the core benefit is still gaining that noise-free state observation for training efficiency.

Dev: From a latency view, I'm curious how fast this pseudo-tactile signal processing needs to be; if the feedback loop is too slow, we lose all our control over those rapid grasping movements.

Rosa: The implementation detail mentions using the Diffusion Policy with an input of the three hundred twenty times two hundred forty RGB image, end-effector 6DoF pose, and that binary gripper state, which confirms it’s designed for real-time processing within a typical policy framework.

Taro: Considering all this, the biggest implication seems to be making manipulation policies much more reliable in unstructured settings because they are less dependent on perfect tactile sensing or huge amounts of real-world data.

Title and authors: Dev: I think the most significant impact is reducing the dependency on expensive, time-consuming human demonstration data collection, which could drastically lower the cost and effort for training new manipulation skills.

Rosa: That really hits home; if we can train policies effectively in simulation using this cleaner observation method, it means we spend less time and money gathering real-world interaction data to get those robots capable of complex tasks.

Taro: I think it’s about shifting the focus from perfect state estimation to robust state inference, which is a very practical step for achieving true autonomy when things go wrong.

Dev: I'm still thinking about the sim-to-real transfer; if the policy learns based on this clean binary observation in simulation, how well does that translate when we introduce those kinematic differences in physical hardware?

Rosa: The paper tackles that by combining the state-based expert policy for automatic data collection in simulation with admittance control during real-world deployment to manage those discrepancies.

Taro: So, it seems like they’ve built a layered approach: improving the observation quality first, and then adding a compliant control layer for deployment.

Dev: That compliance layer is interesting because it addresses the physical mismatch between simulation dynamics and real-world constraints when interacting with things like oven doors.

Rosa: It sounds like "Disambiguate Gripper State in Grasp-Based Tasks: Pseudo-Tactile as Feedback Enables Pure Simulation Learning" provides a solid way to enhance robustness without needing additional, costly hardware or extensive real-world data collection for training.

Taro: I think the future of this research points toward policies that are inherently more resilient because they can correctly interpret their state even when sensor data is ambiguous or noisy.

Dev: For me, the implication is a much faster iteration cycle for policy development because we aren't bottlenecked waiting for perfect real-world demonstrations to refine our training data.

Rosa: Indeed, and I think this paper gives us a tangible path toward building more capable robotic systems that can operate effectively in complex, messy environments with less reliance on perfect sensor calibration.

Taro: That’s the big picture; moving toward systems that are inherently more self-correcting in their interaction with the environment.

The paper's summary: Rosa: So, basically, this paper proposes using force feedback from the gripper itself to figure out if an object is actually grasped or not, which lets them use a simple binary state for training in simulation instead of a messy continuous one.

Dev: That’s right; they’re essentially designing this closed-loop system where the physical deformation of the fingers gives them that pseudo-tactile signal to override what their standard binary observation tells them when things get tricky.

Taro: What I find really interesting is how they handle those failures during inference; it sounds like when the gripper closes but doesn't actually grab anything, this feedback forces it to open immediately instead of letting the policy try something wrong, like pulling.

Rosa: Exactly, that ability to self-correct during operation without needing new data collection is a big deal for real-world reliability. It means they can use simulation data—which is cheap and abundant—to train policies that are already robust against those kinds of errors when they face actual physical disturbances.

Dev: From an engineering standpoint, the fact that this method enables pure simulation learning bypasses the need to constantly collect new, potentially noisy real-world data just to fix edge cases; it lets them leverage the simulation's advantages like domain randomization without worrying about gripper disturbance during training.

Taro: That ability to train policies effectively in simulation while maintaining resilience when deployed is a significant step toward making robotic systems more trustworthy in unpredictable environments. It addresses that sim-to-real gap by giving the policy a cleaner way to interpret its physical state.

Rosa: And they showed this works across several different grasp tasks, like picking things up and opening drawers, which suggests it’s not just a lab curiosity but has broad applicability in common manipulation scenarios.

Dev: I'm still focused on the performance metrics; they report a one hundred percent disturbance resilience success rate across those tests, which is pretty strong for real-world tasks. My main concern is how long this system can reliably operate in the field before that pseudo-tactile feedback degrades due to wear or environmental factors.

Taro: That’s a fair question, Dev; the paper mentions they used admittance control to help manage those kinematic discrepancies when moving from simulation to the real world, which suggests they've built some safeguards against physical mismatches.

Rosa: So, while the hardware implementation is key for long-term field use and handling those kinematic issues during transfer, the core finding is that this technique dramatically improves how robust a policy can be when it interacts with an object.

Dev: It sounds like the main implication here is reducing the dependency on expensive real-world human demonstrations for training, which should make developing new manipulation skills much more cost-effective and scalable.

Taro: I think the broader impact is shifting focus toward making policies that are inherently smarter about their own state estimation, rather than just relying on perfect sensor readings. That capability to infer a successful grasp from partial force information could be very useful in areas where high-fidelity tactile sensors aren't practical yet.

The paper's improvements: Rosa: To recap, the core improvement suggested by this paper is moving away from relying on noisy continuous joint angle observations by incorporating that pseudo-tactile feedback to create a clean binary state for training.

Dev: That's right; they advocate for replacing those imperfect signals with this controlled output because it lets the AI learn directly from a much clearer observation, which simplifies the policy's task immensely.

Taro: What I really dig is how they suggest this approach helps mitigate that sim-to-real gap by ensuring the policy doesn't get confused by visual cues when moving to a physical robot.

Rosa: Exactly; they show that this technique allows policies trained in simulation to perform better in the real world because they aren't relying on unreliable visual data for state estimation, which is a huge win for deployment.

Dev: From my side, the improvement lies in creating a more stable learning environment where the policy isn't constantly having to guess if it has an object or not, which should naturally lead to better control loop stability and lower latency during execution.

Taro: It means that when the AI encounters an unexpected physical situation—say, a slight slip or a premature closure—it doesn't just fail; it can use this feedback mechanism to immediately correct its action and reattempt the grasp properly.

Rosa: That self-correcting behavior is what makes the system so robust, and they suggest that this capability is necessary for handling tasks like oven opening where precise force management is critical.

Dev: I'm interested in how practical this feedback loop is; if we need a very high update rate, can we implement that pseudo-tactile signal without adding significant computational overhead to the control architecture?

Taro: The paper implies it’s designed to be integrated smoothly into existing policy frameworks, which suggests it should work within current real-time constraints, even if the precise implementation depends on the hardware.

Rosa: So, these suggested improvements point toward a future where robotic policies are inherently more resilient because they can interpret their interaction with objects through this richer feedback mechanism rather than just relying on raw sensory input.

Dev: It’s about making the policy smarter about its own state interpretation, which should lead to more predictable and reliable system behavior under varying conditions.

Taro: If we can achieve this level of state inference robustness, it opens up a lot of possibilities for autonomous systems that operate in unstructured environments where perfect sensing isn't always available.

Conclusion: Rosa: So, to wrap things up on "Disambiguate Gripper State in Grasp-Based Tasks: Pseudo-Tactile as Feedback Enables Pure Simulation Learning," we've seen how using pseudo-tactile feedback lets policies learn from clean binary observations, which really boosts robustness.

Dev: I agree; the ability to use pure simulation data for training is a massive step forward for reducing the need for expensive real-world interaction time.

Taro: I think it shows that we can build autonomy where systems are inherently more self-correcting because they don't have to rely on perfect sensory input to know what they’re doing.

Rosa: It really puts the focus on making policies smarter about interpreting physical states rather than just following a set of pre-defined rules, which is vital for complex manipulation.

Dev: I think the main implication is that we can train more reliable systems faster, provided we can engineer that pseudo-tactile feedback loop to run fast enough and reliably under real operating conditions.

Taro: I'm still thinking about how this could help in areas where physical sensing is limited; if you can infer success from force equilibrium alone, that opens up new avenues for less hardware-intensive autonomy.

Rosa: Exactly, so the potential impact here is making manipulation tasks much more reliable and efficient across a huge range of scenarios.

Dev: We should keep an eye on how long this method holds up in prolonged field use, as I mentioned earlier, because the physical sensors themselves will eventually degrade under constant stress.

Taro: That's a practical consideration for long-term deployment; we need to see if the pseudo-tactile signal remains valid over extended periods of operation.

Rosa: Well, that covers it for this paper; "Disambiguate Gripper State in Grasp-Based Tasks: Pseudo-Tactile as Feedback Enables Pure Simulation Learning" gives us a solid foundation for more resilient policies.

Dev: Indeed; it’s a great piece of work that bridges the gap between simulation training and real-world robustness through clever feedback design.

Taro: I look forward to seeing how researchers build on this concept to expand its application beyond simple grasp tasks into more complex, dynamic scenarios.

State Key Laboratory of Industrial Control and Technology, Zhejiang University

cs.RO

Submitted: 2025-03-31

Updated: 2026-10-05

Comments: 8 pages, 5 figures, accepted by IROS 2025, project page: https://yifei-y.github.io/project-pages/Pseudo-Tactile-Feedback/

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 77/100

The gist: Grasp-based manipulation tasks are fundamental to robotics, but ambiguity in gripper state significantly reduces the robustness of imitation learning policies.

Key concepts

Gripper State Ambiguity
The standard binary observation of a gripper state is unreliable because it cannot tell the difference between a successful grasp and an empty gripper. This lack of information makes policies fragile when faced with unexpected physical disturbances, leading to incorrect actions.
Pseudo-Tactile Feedback
This technique treats the force-controlled gripper as a tactile sensor. When grasping occurs, the resulting deformation in the fingers (represented by joint angle) provides this 'pseudotactile information,' helping the system understand if an object has actually been grasped.
Pure Simulation Learning
This method allows policies to be trained entirely within a simulated environment using data generated from that simulation. By fixing the gripper state ambiguity, the policy can leverage simulation's advantages, like domain randomization and access to privileged states, leading to better real-world performance.
Admittance Control
This control method allows the robot to adjust its motion dynamically based on external forces and torques. It solves a dynamic model that balances inertia, damping, and stiffness. This compliance helps the robot accurately track desired movements while reacting safely to unexpected physical interactions in the real world.

Terminology

Summary

Grasp-based manipulation tasks are fundamental to robotics, but ambiguity in gripper state significantly reduces the robustness of imitation learning policies. The core finding is that employing pseudo-tactile feedback from a force-controlled gripper can disambiguate this state, allowing policies to utilize noise-free binary observations and enabling pure simulation learning to enhance real-world performance.

The Gist

We propose a novel approach employing pseudo-tactile as feedback to disambiguate the gripper state in grasp-based tasks, enhancing the system’s robustness without additional data collection and hardware involvement.

Problem Identification: Gripper State Ambiguity

Gripper state ambiguity arises because the binary gripper state observation cannot distinguish between a successful and an empty grasp. In demonstrations, a closed gripper always indicates a successful grasp, but this correlation breaks under perturbations during inference. The root cause is the lack of tactile feedback to the policy; while human operators rely on tactile feedback to determine success, the policy mistakenly treats the feedforward binary gripper state as reliable. This mismatch makes policies vulnerable to disturbances, such as forcing a gripper closure before successfully grasping an object, leading them to output incorrect actions like a pull instead of reattempting the grasp.

The Proposed Solution: Pseudo-Tactile Feedback

The paper introduces pseudo-tactile feedback by viewing a force-controlled two-finger gripper as a tactile sensor. When the gripper grasps an object and reaches force equilibrium, the deformation of the fingers, represented by the gripper joint angle, provides pseudotactile information. The key innovation is designing a closed-loop gripper controller with pseudo-tactile information as feedback that converts an empty close state into the empty open state. Specifically, when the gripper closes but the pseudo-tactile signal indicates no object has been grasped (i.e., the joint angle reaches its maximum value), the controller overrides high-level policy commands and forces it to open, ensuring that a binary gripper state observation of '1' corresponds only to a successful grasp (grasp close).

Enabling Pure Simulation Learning

By disambiguating the gripper state, policies can utilize the noise-free binary gripper state as an observation, which facilitates pure simulation learning. This allows the policy to benefit from the advantages of simulation, including access to the privileged state of the environment, painless domain randomization, and so on. The proposed method eliminates the need for using continuous gripper joint angles as an observation and avoids gripper disturbance during data collection because real-world inference aligns with simulation data. This enables training policies using pure simulation data to outperform baselines utilizing real-world teleoperation data.

Experimental Validation and Contributions

The approach was tested across three real-world grasp-based tasks: pick-and-lift, drawer-opening, and oven-opening. The results demonstrate that the policy trained with pure simulation data coupled with pseudo-tactile feedback outperforms the baselines trained with real-world teleoperation data across all metrics. Specifically, the method achieved a 100% Disturbance Resilience Success Rate across all tasks. Furthermore, ablation studies showed that incorporating pseudo-tactile feedback is Necessary and Beneficial, as it improves success even under undisturbed conditions by allowing the policy to immediately attempt a grasp again after an accidental closure. The overall contribution is summarized in three key points:

  1. Proposing a novel pseudo-tactile as feedback approach to disambiguate gripper state, enhancing robustness without additional data collection and hardware involvement.

  2. Allowing the use of a binary gripper state, which enables policy learning with pure simulation data, thus taking advantage of the various benefits of simulation.

  3. Conducting experiments across three real-world grasp-based tasks, highlighting the effectiveness of the proposed approach.

Mitigating Sim-to-Real Gap

The paper addresses the sim-to-real gap by using a state-based expert policy for automatic data collection in simulation, which includes randomization over object pose, robot pose, lighting, [and] texture. To further mitigate kinematic discrepancies when transferring to the real world, the method employs admittance control. Admittance control allows the robot to adjust its motion in response to external force and torque by solving the dynamic model: M (¨xc − x¨d) + D (˙xc − x˙ d) + K (xc − xd) = Fext, ensuring accurate tracking while providing compliance to external forces. This combination allows the policy trained on simulation data to perform effectively in real-world tasks under perturbed conditions.

Implementation Details

The policy utilized is the Diffusion Policy (DP), which takes inputs including a third-view 320x240 RGB image, the end-effector 6DoF pose, and the binary gripper state. The training involves using DDIM as the noise scheduler. The action consists of both "the end-effector 6DoF pose and the binary gripper state.

Improvements for AI systems

Here are the specific improvements that can be made to existing AI systems, based on this research, and what these improved systems will be able to do:

  1. Improvements in Policy Robustness via Pseudo-Tactile Feedback Integration:

  2. Enable Pure Simulation Learning for Grasping Policies:

  3. Enhance Real-World Deployment of Simulation-Trained Policies with Reduced Data Costs:

  4. Mitigate Sim-to-Real Gaps in Articulated Object Manipulation (e.g., Oven Opening):

  5. Improved AI System Capabilities (Specific Outcomes):

The proposed system, leveraging pseudo-tactile feedback to disambiguate gripper state, will result in the following specific capabilities:

  1. It will exhibit a near-perfect disturbance resilience rate (as shown by the 100% SR-R metric) in real-world grasp tasks, meaning it can successfully recover from unexpected events like slippage or premature closing of the gripper without needing human intervention or new data collection.

  2. It will achieve superior performance in task completion and grasp success rates compared to policies trained solely on real-world teleoperation data, even when subjected to physical disturbances during inference.

  3. It will enable the training of high-performing manipulation policies using only cost-effective simulation data, circumventing the need for expensive and time-consuming real-world human demonstrations and reducing reliance on costly real-world data collection (e.g., collecting 1000 oven-opening demonstrations in simulation vs. 50 in reality).

  4. It will maintain high efficiency, as the policy trained with pure simulation data consistently requires the least amount of time to succeed across various tasks compared to baselines trained with real-world data or models relying on noisy continuous joint angle observations.

  5. It will effectively handle kinematic discrepancies inherent in articulated objects (like oven doors) by using a noise-free binary gripper state observation, ensuring the policy does not rely on unreliable visual cues for state estimation, thus improving performance under the sim-to-real gap when transferring to physical hardware.

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