Screw Geometry Meets Bandits: Incremental Acquisition of Demonstrations to Generate Manipulation Plans

arXiv:2410.18275 · cs.RO, cs.AI · Submitted 2024-10-23 · 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: "Screw Geometry Meets Bandits".

Dev: A novel approach to systematically obtain a sufficient set of kinesthetic demonstrations for complex manipulation tasks, one example at a time, is presented.

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

Paper summary: Rosa: So we're diving into this paper today, "Screw Geometry Meets Bandits: Incremental Acquisition of Demonstrations to Generate Manipulation Plans." It sounds like they've tackled that tricky problem of knowing when you have enough examples for a robot to actually perform a complex manipulation task reliably.

Dev: Exactly, Rosa. The core idea here is that they're creating a measurable way to check if the demonstrations are sufficient, which is something that has been open for a long time in learning from demonstration research one <ref:2410.18275#pg0>. They claim their approach systematically obtains these demonstrations one at a time, which is pretty ambitious.

Taro: I'm interested in how this relates to autonomy when things go wrong. If the robot can't generate a plan, what does that mean for its behavior when the environment deviates from the expected setup? Does this method help it recover?

Rosa: That’s a great question, Taro. The paper proposes using screw geometry to turn the demonstrations into something concrete that allows for manipulation planning <ref:2410.18275#pg2>. This geometric representation lets them define manipulation constraints based on constant screw segments within the task space, which helps measure sufficiency in a tangible way.

Dev: From an engineering standpoint, I wonder about the computational cost of this geometric decomposition and interpolation, specifically when we're thinking about real-time execution. The paper mentions they use "screw linear interpolation" or ScLERP to generate plans from these guiding poses <ref:2410.18275#pg2>. How does that interact with a tight loop rate?

Taro: It seems like the whole point is finding the right set of demonstrations, not necessarily optimizing the execution speed itself. But if we're talking about failure modes, how does this incremental sampling strategy help us understand where those failures are happening in the task space?

Rosa: That’s where they introduce multi-armed bandit optimization to guide their search for new demonstrations <ref:2410.18275#pg1>. They partition the task space into regions, and they use samples from these partitions to estimate the probability of success in each region, which helps them decide where to ask a human teacher for more data <ref:2410.18275#pg1>.

Dev: So they're essentially using PAC-learning techniques from multi-armed bandits to figure out which areas of the workspace are currently underrepresented by their demonstration set <ref:2410.18275#pg1>. That sounds like a solid way to prioritize the data acquisition process based on uncertainty rather than just picking random areas.

Taro: And what happens when we identify one of those weak regions, say X j, and we get that low estimated probability? Does that mean the robot is fundamentally incapable of handling that specific configuration, or is it just an area where our current demonstrations are sparse?

Rosa: The paper defines sufficiency probabilistically: a set of demonstrations is sufficient if the probability of generating successful manipulation plans for task instances drawn uniformly from the whole task instance set exceeds some threshold beta <ref:2410.18275#pg1>. This gives them a formal way to define what "good enough" means for the entire workspace they are interested in.

Paper summary: Dev: That probability measure is coarse, as the paper itself notes, meaning there could still be pockets where we can't generate any successful plan even if the overall probability seems high <ref:2410.18275#pg1>. My concern is that this probabilistic measure doesn't immediately tell us about the latency or jitter in generating those plans during actual operation.

Taro: The implication for autonomy, then, is that we aren't just hoping for the best with a fixed set of demonstrations; we have a systematic way to iteratively improve our knowledge until we are confident about the robot's ability across the whole space <ref:2410.18275#pg0>. This suggests an active learning loop rather than just passive data collection.

Rosa: It moves the process from being purely empirical to being systematically guided by geometric constraints and probabilistic evaluation, which is a big step for field applicability, I think <ref:2410.18275#pg0>.

Dev: It does sound promising for robustness, but the authors themselves flag a limitation: they acknowledge that as the number of task-relevant objects grows, the number of regions can grow exponentially Future Work section. That exponential growth is something we have to seriously consider when we think about scaling this approach beyond simple tasks like pouring or scooping <ref:2410.18275#pg0>.

Taro: So the authors are pointing out that while the method works well for initial problems, applying it to highly complex, high-dimensional environments might require more sophisticated sampling methods than what's described here Future Work section.

Rosa: Right, and they also plan to study whether demonstrations collected in one context can be effectively reused in a completely different environment Future Work section. That would be huge if that holds up.

Dev: From my side, I’m focused on the practical implementation of the iterative acquisition loop. If we are constantly prompting a human teacher based on these bandit results, we need to make sure that the latency introduced by getting that new demonstration doesn't derail our entire control loop <ref:2410.18275#pg1>.

Taro: It seems like the main impact here is providing a framework for creating robust manipulation skills through active, data-driven teacher interaction, which could be very useful for deploying robots in unstructured settings where perfect pre-programming isn't possible <ref:2410.18275#pg0>.

Rosa: So to wrap up this segment on "Screw Geometry Meets Bandits: Incremental Acquisition of Demonstrations to Generate Manipulation Plans," it’s about using geometry to measure sufficiency and bandits to intelligently seek out the missing pieces of demonstration data <ref:2410.18275#pg0>.

Dev: And we've touched on how this active acquisition loop contrasts with traditional methods, even while acknowledging the scaling challenges as noted in their future work <ref:2410.18275#pg0>.

Taro: It really shows a path toward building autonomy that can adapt and confirm its capabilities incrementally, which is something we need as robots move out of the controlled lab setting <ref:2410.18275#pg0>.

Rosa: That's what I was hoping to hear—a systematic way for these systems to build confidence in their physical actions through active learning, and that's a lot of excitement for the field.

Conclusion: Rosa: That seems like a really neat way to handle the problem of needing demonstrations without having to just guess or rely on endless manual tuning.

Dev: I think the authors' main point is that they've formalized sufficiency using screw geometry, which gives them a concrete mathematical basis for planning, and then they layer on bandit optimization to systematically fill in any gaps in their knowledge.

Taro: The real implication here for autonomy is moving away from just having a fixed set of instructions; instead, the system actively learns what it doesn't know by intelligently seeking out demonstrations from human teachers when it hits uncertainty.

Rosa: I wonder how this translates to the real world; could a robot use this method in a factory setting where tasks are constantly changing and new objects appear?

Dev: That’s the million-dollar question, Rosa; the paper shows it works well for pouring and scooping, but as Taro mentioned, they admit that scaling to many different object types could lead to an exponential explosion in the search space that their current bandit framework might struggle with.

Taro: Exactly; if you have a huge number of possible objects, defining those partitions X j becomes incredibly complex, so the future work on adaptive sampling methods seems necessary to handle that scale.

Rosa: It sounds like while this method is very effective for confirming task capability in a controlled environment, we need to see how robust it stays when things get truly unstructured and unpredictable outside of a lab.

Dev: My main concern remains the latency introduced by the iterative acquisition process; if we’re constantly pausing to get new human input, that loop rate needs to be extremely tight for real-time operation.

Taro: The authors are also looking into whether demonstrations learned in one area can actually be applied successfully in a different environment, which would be a big step toward generalizable autonomy.

Rosa: It really shows how we can build confidence in robotic skills through active learning rather than just passively collecting data, and that's a powerful direction for field deployment.

Dev: It’s definitely an interesting framework for incrementally building skill confidence, but the practical hurdle of integrating this acquisition loop into a fast control system is something engineers will have to tackle.

Taro: So, the big picture here is moving toward autonomous systems that can be both capable and self-aware enough to know exactly what they need to learn next.

Dept. of Computer Science, Stony Brook University

cs.RO, cs.AI

Submitted: 2024-10-23

Updated: 2026-10-03

Comments: Published in: 2026 IEEE International Conference on Robotics and Automation (ICRA) ---- External Link: https://doi.org/10.1109/ICRA57385.2026.11696870

DOI: 10.1109/ICRA57385.2026.11696870

License: http://creativecommons.org/licenses/by-nc-nd/4.0/

Importance score: 62/100

The gist: A novel approach to systematically obtain a sufficient set of kinesthetic demonstrations for complex manipulation tasks, one example at a time, is presented.

Key concepts

Screw Geometry
This mathematical tool describes motion constraints in 3D space using screws. It allows the robot to formally define the fundamental motion rules of a task based on recorded human movements. By decomposing demonstrations into 'constant screw segments,' the system can generate precise, constrained plans.
Sufficiency and Coverage
Sufficiency is measured probabilistically: a set of demonstrations is sufficient if there's a high probability (threshold β) that the robot can succeed on any task instance drawn from the whole task set X. Coverage quantifies this by measuring the volume of task instances where at least one demonstration works, ensuring comprehensive coverage.
K-arm Bandit Optimization
When demonstrations are insufficient, the robot treats each region of the task space as an 'arm' in a bandit problem. The reward is gained by sampling a task instance in that region and finding it impossible to plan successfully. This optimization directs the robot to sample from the partitions that are currently least covered by existing demonstrations.
ScLERP
Screw Linear Interpolation (ScLERP) is a planning technique used to generate movement paths based on the task's screw geometry. It ensures that generated plans inherently satisfy all manipulation constraints derived from the demonstrations without needing explicit, separate checks for those constraints.

Terminology

Summary

A novel approach to systematically obtain a sufficient set of kinesthetic demonstrations for complex manipulation tasks, one example at a time, is presented. This method leverages screw geometry to measure demonstration sufficiency and employs multi-armed bandit optimization to incrementally seek additional demonstrations from human teachers until the robot can generate high-confidence manipulation plans across the entire task space.

The gist

A novel approach is presented for the robot to incrementally and actively ask for new demonstration examples until the robot can assess with high confidence that it can perform the task successfully.

Screw Geometry Based Motion Planning

The paper establishes a way to formally define sufficiency by using screw geometry to generate manipulation plans from demonstrations. A kinesthetic demonstration, recorded as a sequence of joint angle configurations in joint space, is represented in task space as a path of poses in SE(3). This path is decomposed into constant screw segments, which are the fundamental motion constraints characterizing the task. The planner uses these guiding poses and then applies screw linear interpolation (ScLERP) to generate a plan. The paper asserts that ScLERP ensures that the manipulation constraints are satisfied without explicit enforcement, thereby providing a concrete way to measure sufficiency: a demonstration is sufficient if the plan can be generated while ensuring joint limits are also satisfied.

Defining Sufficiency and Coverage

The sufficiency of a set of demonstrations is defined probabilistically. Given a task instance set X and a threshold β, the paper states that a set of demonstrations is sufficient if the probability that we can generate successful manipulation plans for task instances uniformly drawn from X exceeds β. This leads to defining Coverage as the ratio of the volume of task instances where at least one demonstration works to the total volume: PX (D) = Vol(B(D, X)) / Vol(X). A set D is then deemed sufficient if PX (D) ≥ β.

Identification of New Demonstration Candidates via Bandit Optimization

When a set of demonstrations D0 is insufficient, the paper partitions the task space X into K disjoint compact sets, denoted as Xj. The problem of identifying which region needs attention is formulated as a K-arm bandit optimization problem. The reward for pulling arm j corresponds to sampling a task instance x from partition Xj and yielding a reward of 1 if the current set of demonstrations cannot generate a successful manipulation plan for x (i.e., if x is in the set where the robot has low estimated probability of generating successful manipulation plans). The goal is to find the best arm, which corresponds to the partition that is least covered by Di.

Incremental Acquisition using Self-Evaluation

The process of acquiring new demonstrations follows a structured iterative algorithm. In each step, the algorithm first determines the partition j∗ that is least covered by the current set of demonstrations using bandit optimization (Algorithm 2). These samples are used to select a suggested task instance y∗ for the next demonstration. A new kinesthetic demonstration is then obtained from a human teacher based on this suggested instance. This new demonstration is added to D, and the process repeats until the stopping condition is met: when with high confidence, the current set of demonstrations D is sufficient for all partitions. The stopping condition relies on an empirical estimate of failure probability in the optimal region, specifically when µˆj∗ ≤ 1−ϵ−β, which implies that PXj(Di) ≥ β with confidence (1 − δ).

Experimental Validation and Results

The approach was validated on two tasks: pouring and scooping. Experimental results show that only a handful of examples, at most 7, are always sufficient for these tasks. The paper demonstrates the interactive process where, after acquiring one demonstration, the algorithm picks the region with the highest failure probability to select the next example. Furthermore, it shows that while a smaller number of regions K might require fewer demonstrations overall for an overall success guarantee, a higher K is necessary to identify those pockets of failure and succeed in each individual region. The robot successfully executed plans generated from these sufficient demonstrations on physical robots without observing execution failures related to joint limits.

Future Work

The authors note that as the number of task-relevant objects increases, the number of regions grows exponentially, suggesting that future work will require a combination of non-naive sampling methods such as adaptive sampling [35], and hierarchical methods to focus on smaller sets of promising candidates. Additionally, they plan to study whether demonstrations given in one context can be reused in a new environment.

References

[1] B. D. Argall, S. Chernova, M. Veloso, and B. Browning, “A survey of robot learning from demonstration,” Robotics and autonomous systems, vol. 57, no. 5, pp. 469–483, 2009

[2] S. Chernova and A. L. Thomaz, Robot learning from human teachers.

Improvements for AI systems

Based on the provided scientific paper, here are specific improvements that can be made to AI systems, along with what these improved systems could achieve:

) Plan Generation from Demonstrations via Screw Geometry (ScLERP):

The system should replace generic motion planning techniques with one based on the screw geometry representation of demonstrations. This ensures that manipulation plans generated from a set of demonstrations are mathematically guaranteed to preserve the task-related constraints (like maintaining an object upright or following a specific path around an axis) encoded in the demonstration, even when interpolating between demonstration points.

) Self-Evaluation Framework for Demonstration Sufficiency:

The AI system should implement a formal framework that allows it to quantitatively measure whether its current set of demonstrations is sufficient to cover the entire task space (or workspace). This involves defining Coverage as the volume of task instances where at least one demonstration works, and Sufficiency based on this coverage exceeding a threshold.

) Multi-Armed Bandit (MAB) Optimization for Active Demonstration Seeking:

The system should employ a PAC-learning approach, specifically a Multi-Armed Bandit strategy, to systematically search for the most informative next demonstration. Instead of randomly seeking new data, the AI should use the MAB formulation to identify which subregion of task instances is currently least covered (i.e., where its ability to generate successful plans is lowest).

) Incremental and Targeted Demonstration Acquisition:

The system should operate iteratively, following a structured acquisition loop:

  1. Identify the region of highest uncertainty/lowest coverage using the MAB strategy.

  2. Suggest a specific task instance from that region for human demonstration (e.g., Please show me how to pour this specific amount into this bowl).

  3. Collect the new kinesthetic demonstration data, which is then used to update the knowledge base of successful plans.

) Heuristic-Driven Demonstration Selection:

When seeking a new task instance from a region of weakness, the system should use a heuristic (e.g., selecting the task instance where a joint-limit violation occurred in an earlier segment of the demonstration path) to guide the human teacher toward providing demonstrations that address specific failure modes.

The improved AI system can achieve:

  1. A robot capable of performing complex, constrained manipulation tasks (like pouring or scooping) with high confidence across its entire operational workspace, even when only a small number of demonstrations are provided.

  2. The ability to autonomously ask for the exact kinesthetic guidance needed to cover previously unmastered regions of task space, rather than relying on pre-programmed trajectories or generic imitation.

  3. A robust and verifiable method for assessing its own competence in generating feasible motion plans under physical constraints (joint limits), leading to safer and more reliable robotic execution in unstructured environments.

Abstract

In this paper, we study the problem of methodically obtaining a sufficient set of kinesthetic demonstrations, one at a time, such that a robot can be confident of its ability to perform a complex manipulation task in a given region of its workspace. Although programming by demonstration has been an active area of research, the problems of checking whether a set of demonstrations is sufficient and systematically seeking additional demonstrations have remained open. We present an approach for the robot to incrementally and actively ask for new demonstration examples, one at a time, until the robot can assess with high confidence that it can perform the task successfully. Our approach uses: (i) a screw geometric representation of motion to generate manipulation plans from demonstrations, which makes the sufficiency of a set of demonstrations measurable; (ii) a sampling strategy based on PAC-learning from multi-armed bandit optimization to evaluate the robot's ability to generate manipulation plans in a subregion of its task space; and (iii) a heuristic to seek additional demonstration from areas of weakness. We present results of a user study conducted with 22 participants (without any background in robotics) on two example manipulation tasks, namely pouring and scooping, to assess the utility and usability of our approach. The results show that a handful of examples (fewer than 10) were needed to successfully teach the robot to plan tasks. A short video supplement is available on YouTube: https://youtu.be/KbAPgIouIvo

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