FuncBridge: Towards Functional Tool-Use Generalization via Keypoint Trajectory Reasoning

arXiv:2607.05780 · cs.RO, cs.AI, cs.CV · Submitted 2026-07-07 · 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: "FuncBridge: Towards Functional Tool-Use Generalization via Keypoint Trajectory Reasoning".

Dev: Functional generalization in robotic tool-use—the ability to repurpose tools for novel functions despite differing motor patterns—is a critical challenge that current policies fail to address,

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

Title and authors: Rosa: So, we're diving into the paper "FuncBridge: Towards Functional Tool-Use Generalization via Keypoint Trajectory Reasoning." This work tackles that tough problem of robots being able to use a tool for a new job even if they've never seen that specific tool before.

Dev: That sounds like it’s tackling functional generalization, Rosa, which is really the core issue when we try to move beyond just learning how to perform one specific task with one specific gripper.

Taro: Exactly; it moves past visual similarity and forces the system to understand the actual physical intent behind using a tool, which is what I always think is missing in current autonomy setups when things get messy.

Rosa: Right, and this paper proposes a specific intermediate representation to bridge that gap, which they call 2D keypoint trajectories. It suggests these trajectories are better than just looking at the tool's appearance or general videos for capturing what the robot needs to do functionally.

Dev: I see what they mean; they're trying to find something that has enough information about where to touch and how much force to apply without getting bogged down by the exact shape of the object in front of it, which is a common failure mode.

Taro: And by focusing on keypoint trajectories, they are aiming for something that stays grounded in geometry while still being expressive enough for different tools, which addresses that fundamental mismatch they describe.

Rosa: It seems like the main contribution here is their two-stage policy called FORGE, which splits the task into predicting the general plan and then executing it precisely.

Dev: Decoupling reasoning from execution sounds smart for managing complexity; if you can predict a functional trajectory first, then you just need a reliable way to map that prediction into actual motor commands without getting lost in every visual detail.

Taro: From an autonomy standpoint, that separation is important because it lets the system handle unexpected situations during execution by relying on the pre-learned functional plan rather than trying to re-reason about the whole task from scratch mid-motion.

Rosa: They train the first stage on action-free data to learn those transferable functional intents, and then they fine-tune a second stage using smaller datasets just to nail down the final movements for execution.

Title and authors: Dev: That’s a smart way to use data asymmetry; learning the high-level motion priors from vast amounts of action-free data and only using labeled demonstrations for grounding seems like a practical way to handle limited labeled data effectively.

Taro: I wonder how robust this is when the world throws unexpected physical interactions at it, like if the tool slips or if the target moves slightly during execution; does that two-stage approach provide enough recovery mechanisms?

Rosa: That's a key question for me, Taro; we need to know how well it handles those deviations outside of perfect lab conditions.

Dev: If the loop rate is fast enough and the grounding policy is robust, I think it could handle some real-world noise, but we have to be mindful of the latency introduced by that two-stage process during rapid interaction.

Taro: I'm curious about what happens when the world misbehaves; does this system have a mechanism to adapt its functional reasoning if the predicted trajectory immediately fails upon contact?

Rosa: The paper shows they tested this in real-world settings, and their results suggest significant improvement over previous methods, even when dealing with novel tools.

Dev: The success rates they report are pretty compelling; seeing an average success rate of over two times the performance on unseen tools is a big indicator that this representation works better than just looking at appearance.

Taro: If we can generalize functional intent across different physical objects, that opens up possibilities for robots to handle much more varied tasks in dynamic settings, which is what I’m really interested in for future autonomy.

Rosa: So, before we move on to how they actually designed this FORGE system, let's touch on what specific improvements the authors suggest based on their evaluation of different representations.

Dev: I'm looking forward to hearing about those suggested tweaks, especially regarding how they might make the execution grounding part more stable in a live control loop.

Taro: I want to hear if they flag any limitations where this keypoint trajectory approach might fall short when the physical interaction gets very complex or involves highly constrained dynamics.

Rosa: Well, it looks like they suggest making sure there's a mechanism for post-processing or smoothness regularization during execution grounding to stop those jerky action failures we often see in real-world robotics.

Title and authors: Dev: That makes sense; if the grounding policy produces too much high-frequency noise, that’s going to cause instability in our control loop, so smoothing it out sounds like a necessary engineering step.

Taro: I also noticed they discuss using the predicted trajectories explicitly for alignment tasks before impact, which is interesting because it suggests a more proactive approach to achieving precision rather than just reacting to contact.

Rosa: That proactive use of keypoint information seems promising for improving precision in hitting tasks; it implies the system is not just aiming blindly but is guiding the tool's motion based on its predicted trajectory.

Dev: If that alignment guidance works, it could significantly reduce the error budget we have to manage during fine motor control when dealing with novel geometries.

Taro: I think if they can maintain that level of functional understanding across a wider variety of physical constraints, this kind of method could be very valuable for complex manipulation scenarios where tools and targets are constantly changing.

Rosa: So, to wrap up on the overall implications and what this means for the broader field, FuncBridge seems to show that abstracting motion into functional keypoint trajectories is a valid path toward achieving genuine tool-use generalization in robotics.

Dev: It really reinforces my view that we need representations that capture intent rather than just pixels or simple geometric shapes to make these systems truly flexible.

Taro: This suggests that future work in autonomy should focus on learning these kind of rich, functional intermediate representations from data without relying on massive amounts of meticulously labeled interaction data for every new tool.

Rosa: It's a solid piece of work, and I'm really excited about seeing how these ideas translate into more robust, adaptable robotic systems in the lab and eventually out there.

Dev: I’m just keeping an eye on the performance metrics reported in simulation versus real-world settings to gauge exactly where this method shines and where we still need to focus our engineering efforts.

Taro: We really need to keep pushing for systems that can handle the messy, unpredictable nature of physical interaction without needing a completely re-trained policy every time they encounter something new.

The paper's summary: Rosa: So, FuncBridge is essentially proposing a way for robots to stop looking at just what an object looks like and start reasoning about its function to use any tool, no matter how new it is.

Dev: That’s the core idea, Rosa; they’re building this two-stage system where the first part figures out the general functional plan based on movement patterns, and the second part makes sure that plan actually translates into precise motor commands for hitting a target.

Taro: And what really caught my attention is their choice of 2D keypoint trajectories as that middle layer; they argue those trajectories are compact enough to hold the necessary information about how to interact with a tool while ignoring distracting visual details.

Rosa: Exactly, Taro; they’re suggesting these keypoints capture the geometric and motion structure needed for function without getting stuck on the specific appearance of a particular object.

Dev: From an engineering standpoint, that decoupling sounds smart because it lets us train the high-level reasoning part on massive amounts of data where we don't have labels, and then we only need those smaller labeled sets to fine-tune the execution policy for grounded actions.

Taro: I’m still thinking about how robust this is when things go wrong in the physical world; does this functional trajectory approach handle unexpected slips or sudden changes in contact dynamics well?

Rosa: The authors did test it in both simulation and real-world settings, and they found that their method actually showed a success rate improvement of over two times on unseen tools compared to existing methods.

Dev: That two times improvement is substantial, Rosa; that tells us this approach is significantly better than just relying on direct perception-to-action mapping when dealing with novel tools.

Taro: If this works outside the lab environment and doesn't require a massive retraining cycle every time we introduce a new gripper, that’s what makes it truly impactful for real autonomy.

Rosa: That’s precisely what I want to explore next; we need to look at how they actually set up the training strategy, specifically how they leverage those action-free datasets.

The paper's improvements: Tom: So, FuncBridge suggests several ways to make this system even better, focusing on how we handle execution in the real world and during complex tasks.

Rosa: They are suggesting adding a mechanism for trajectory post-processing or smoothness regularization during execution grounding to prevent those jerky movements that often cause tool dropping failures when things aren't perfect.

Dev: I agree with that; if the policy generates too much high-frequency noise, it’s going to cause instability in our control loop, so smoothing that out seems like a necessary engineering step for reliable operation.

Taro: They also propose using those predicted keypoint trajectories explicitly for alignment tasks before impact, which means the system actively guides the tool's approach based on its predicted path rather than just reacting to where it currently is.

Rosa: That proactive guidance sounds promising; it implies the system isn't just aiming blindly but is using its knowledge of the desired motion to improve precision in hitting tasks.

Dev: If that alignment guidance works consistently, it could significantly reduce the error budget we have to manage during fine motor control when dealing with tools that have slightly different geometries than what was modeled.

Taro: I wonder if they can maintain this level of functional understanding across a wider variety of physical constraints, particularly if the dynamics become highly constrained or unpredictable during contact.

Rosa: That’s a valid concern; we need to know if the system still performs well when the physical interaction gets very complicated and involves tight spatial limitations.

Dev: We'll have to check the latency impact of adding those post-processing steps, because every extra step in the pipeline adds time that could compromise our real-time performance requirements.

Taro: I’m interested if they suggest any way for the functional reasoning part to dynamically adapt its plan if the execution grounding immediately fails upon contact with an unexpected physical response.

Rosa: They did touch on that idea, suggesting a mechanism for the system to adjust its functional representation if it detects a failure mode during execution, which would allow it to recover more gracefully.

Dev: Recovery is crucial; we can’t afford for the system to just crash or drop the tool when encountering unexpected resistance in a live scenario.

Taro: If they can keep that level of functional understanding while adding these safety and refinement layers, this method could be very useful for complex manipulation scenarios where tools and targets are constantly shifting.

Rosa: So, it seems FuncBridge is not just about getting a successful hit rate but also about making the whole process more stable and proactive in its approach to execution.

Conclusion: Rosa: So, we're wrapping up this discussion on FuncBridge, which shows that by focusing on keypoint trajectories as an intermediate representation, AI can learn to use tools for new functions much more reliably than before.

Dev: That’s right; it moves past simple visual imitation to reasoning about the required motion structure across different objects.

Taro: I think the biggest implication is that we might finally get robots that can truly generalize their manipulation skills without needing a brand-new, massive dataset for every single new tool they encounter.

Rosa: And if this works outside the lab for long enough, it means we could see robots performing complex tasks in real-world environments much more flexibly.

Dev: I’m still thinking about the loop rate; while the success rates are high, we need to confirm that the two-stage process doesn't introduce unacceptable latency for fast, dynamic interactions.

Taro: When things get messy and the world misbehaves physically, I hope this functional reasoning keeps the robot from getting stuck in a broken state because it has a general plan to fall back on.

Rosa: It seems like the whole team is really excited about how much better this is than just relying on appearance cues for tool use.

Dev: Exactly; that capability to understand function rather than form is what makes this approach so compelling from a control systems view.

Taro: I’m keen to see how researchers build on this; maybe we can integrate these functional trajectories with the force-adaptive methods we've been looking at for better contact control.

Rosa: We certainly will; it sets a strong foundation for figuring out how to make these robots truly adaptable in any setting.

Dev: Next up, we’re going to look at some papers that tackle the more immediate challenges of real-time planning and how AI can handle those tight control loops.

Nanyang Technological University

cs.RO, cs.AI, cs.CV

Submitted: 2026-07-07

Updated: 2026-10-01

Comments: 19 pages, 12 figures, 6 tables

Project page: https://chuhaozhou99.github.io/FORGE

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

Importance score: 85/100

The gist: Functional generalization in robotic tool-use—the ability to repurpose tools for novel functions despite differing motor patterns—is a critical challenge that current policies fail to address,

Key concepts

Functional Reasoning (System-2)
This first stage predicts generalizable keypoint trajectories from action-free data. It learns to forecast future keypoint motions based on visual observations and the current functional representation, capturing the underlying functional plan for a tool without needing explicit robot actions.
Grounded Execution (System-1)
This second stage takes the predicted trajectories and converts them into precise motor commands. It is trained on labeled data to map these functional keypoint predictions directly into executable robot actions, ensuring the predicted movement translates into physical motion.
2D Keypoint Trajectories
These are used as the optimal intermediate representation. They discard tool-specific appearance while retaining compact geometric information about how function-relevant points move and align over time, such as hitting points and target positions.
Two-Stage Training Strategy
The method trains the functional predictor on large action-free data to learn transferable intent first. Then, it freezes this predictor and fine-tunes the execution policy using a smaller set of labeled demonstrations to ground the predictions into real movements.

Terminology

Summary

Functional generalization in robotic tool-use—the ability to repurpose tools for novel functions despite differing motor patterns—is a critical challenge that current policies fail to address, and this work proposes a method called FORGE to bridge the perception-to-action gap. The core finding is that 2D keypoint trajectories serve as the optimal intermediate representation because they effectively balance functional expressiveness with action groundability, leading to over 2x improvements in average success rate on unseen tools in both simulation and real-world settings.

How it works

The paper addresses the difficulty of transferring functional intent across tools by proposing FunctiOnal Reasoning and Grounded Execution (FORGE), a two-stage policy designed to decouple functional reasoning from action execution. This approach is motivated by the observation that existing generalization methods fail because they focus on visual appearance or category, rather than the underlying functional intent shared between tools.

The FORGE framework operates in two distinct stages:

  1. A first stage, the functional reasoning policy (System-2), predicts generalizable keypoint trajectories from action-free data to capture functional plans for unseen tools. This predictor is trained on a large-scale action-free dataset, learning to forecast future keypoint motions based on visual observations and the current functional representation.

  2. A second stage, the grounded execution policy (System-1), takes these predicted trajectories and grounds them into executable robot actions using a smaller set of action-labeled demonstrations. This policy is trained on action-labeled data to map functional keypoint predictions into precise motor commands.

Functional Intermediate Representation Selection

A central part of the method involves systematically evaluating different intermediate representations to find the best fit for bridging perception and action. The paper evaluates three candidates: affordance images, human video prompts, and 2D keypoint trajectories. The authors hypothesize that 2D keypoint trajectories form a suitable choice of Xt:t+H because they discard tool-specific appearance that may cause overfitting, while retaining compact geometric information such as the hitting point, target position, and affordance-relevant tool structure.

The evaluation confirms this hypothesis through experiments where the execution policy is conditioned on these representations. The results show that 2D keypoint trajectories achieve the best performance because they provide a compact, structured description of how function-relevant points move and align over time, surpassing static region cues and dense visual motion.

Training Strategy and Policy Design

The two-stage training strategy is designed to leverage data asymmetry effectively. The first stage trains the keypoint motion predictor on the action-free dataset DU to capture transferable functional intent as keypoint-based motions, without relying on large-scale robot action labels. The second stage involves freezing this pretrained predictor and fine-tuning the execution policy πsys1 on the smaller, action-labeled dataset DL. This decomposition allows FORGE to learn functional priors from abundant action-free data while using limited labeled data solely for grounding the predicted trajectories into executable actions.

The training objectives are formalized as follows:

  1. For System-2 (Keypoint Motion Predictor): Minimizing a loss function Lsys2 that learns a conditional velocity field uϕ by predicting future keypoint trajectories Xˆ t:t+H given the observation ot and the functional representation xt at time-step t.

  2. For System-1 (Execution Policy): Minimizing a loss function Lsys1 that learns a conditional velocity field vθ to ground the predicted trajectories X˜ t:t+H into executable robot actions Aˆ t:t+H, conditioned on ot, qt, and the functional representation xt.

Experimental Validation and Results

The effectiveness of FORGE is validated through extensive experiments using a seven-tool hitting-function benchmark in both simulation and real-world settings. The paper tests four hypotheses: H1 (functional generalization requires an intermediate representation), H2 (keypoint trajectories are best), H3 (keypoint trajectories must be function-aware), and H4 (the two-stage design generalizes under moderate action-labeled tool diversity).

The results consistently demonstrate FORGE's superiority over state-of-the-art methods. In simulation, FORGE achieved an average success rate of over 2× improvement on unseen tools. In the real world, FORGE outperformed the Flow Matching (FM) baseline by achieving a success rate of 0.36 on the unseen book tool, compared to FM's 0.16 for that tool setting. This performance confirms that direct perception-to-action mapping is insufficient for functional generalization, as FORGE successfully predicts function-relevant keypoint trajectories that specify how the desired hitting region should approach and contact the target.

Ablation Studies

Ablation studies further dissect the design choices.

Improvements for AI systems

Here are specific improvements that can be made to current AI systems, based on the findings of the FORGE paper:

  1. A two-stage policy architecture that decouples functional reasoning from action execution (Functional Reasoning and Grounded Execution). This system first predicts generalizable keypoint trajectories from action-free data and then grounds these predicted plans into executable robot actions using limited demonstrations.

  2. Incorporating 2D keypoint trajectories as the primary functional intermediate representation, as they best balance functional expressiveness (capturing contact region and motion structure) and action groundability (discarding tool-specific appearance that causes overfitting).

  3. Training the prediction module on large-scale action-free datasets to learn transferable functional intent from unseen tools, rather than relying solely on end-to-end mapping or generic trackers.

  4. Implementing a mechanism for trajectory post-processing or smoothness regularization during execution grounding to mitigate failure modes like tool dropping caused by jerky actions.

  5. Utilizing the predicted keypoint trajectories explicitly as guidance for alignment tasks, enabling the system to bring the tool-specific contact region toward the target before impact, leading to higher precision in hitting tasks.

These improvements will enable AI systems (specifically robotic manipulation policies) to achieve:

  1. Adapt seamlessly and successfully to novel tools that share a common functional intent with seen tools (Functional Generalization).

  2. Perform complex hitting or contact-based manipulations accurately across diverse environments, even when the tool geometry is entirely new, by reasoning about the required motion rather than just imitating visual appearance.

  3. Achieve significantly higher success rates (over 2x improvement demonstrated in experiments) in real-world and simulated settings compared to current state-of-the-art methods like end-to-end or generic keypoint tracking policies.

Sources

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