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Shared Autonomy via Hindsight Optimization for Teleoperation and Teaming
Shervin Javdani, Henny Admoni, Stefania Pellegrinelli, Siddhartha S. Srinivasa, J. Andrew Bagnell
TL;DR
Predict-then-act shared autonomy may provide little assistance while the user’s goal remains uncertain, even though useful actions can support multiple possible goals. This paper models assistance as a POMDP and approximates its solution with hindsight optimization, reporting improved objective outcomes across teleoperation tasks and reduced idling and collision in human-robot teaming, while total teaming time is mixed.
Problem
Predict-then-act systems require confident identification of the user’s goal before assisting, but goals may be difficult to specify or predict early in execution.
Method
The framework models the user’s goal as hidden state in a POMDP and uses QMDP, or hindsight optimization, to minimize expected cost-to-go over goal distributions.
Results
The method outperformed predict-then-act across objective measures in both teleoperation tasks, while human-robot teaming showed less idling and collision but mixed total completion time.
Takeaways & Limitations
Assistance can remain useful under goal uncertainty by selecting actions that help across multiple possible goals rather than waiting for a single confident prediction.
Takeaways & Limitations
Hindsight optimization assumes uncertainty resolves at the next timestep and performs poorly when explicit information gathering is necessary.
Abstract
from arXiv · showhide
In shared autonomy, a user and autonomous system work together to achieve shared goals. To collaborate effectively, the autonomous system must know the user's goal. As such, most prior works follow a predict-then-act model, first predicting the user's goal with high confidence, then assisting given that goal. Unfortunately, confidently predicting the user's goal may not be possible until they have nearly achieved it, causing predict-then-act methods to provide little assistance. However, the system can often provide useful assistance even when confidence for any single goal is low (e.g. move towards multiple goals). In this work, we formalize this insight by modelling shared autonomy as a Partially Observable Markov Decision Process (POMDP), providing assistance that minimizes the expected cost-to-go with an unknown goal. As solving this POMDP optimally is intractable, we use hindsight optimization to approximate. We apply our framework to both shared-control teleoperation and human-robot teaming. Compared to predict-then-act methods, our method achieves goals faster, requires less user input, decreases user idling time, and results in fewer user-robot collisions.
1 Introduction
Shared autonomy must assist despite uncertainty about the user’s goal. This work models that uncertainty explicitly and uses hindsight optimization to provide assistance across goal distributions.
- Shared autonomy requires the system to infer the user’s goal while the user and robot pursue shared objectives.
- Explicit goal specification is difficult when goals form a continuum, motivating inference from environmental sensing and user actions.
- Predict-then-act methods may provide little assistance because single-goal confidence can remain low until late in execution.In the feeding experiment, the baseline assisted for 31% of execution time and took 29.4 seconds to reach its confidence threshold.
- The proposed framework models the user’s fixed goal as hidden state in a POMDP and selects assistance useful for a distribution over possible goals.When confidence is low, assistance can progress toward multiple goals; when confidence is high, it focuses on the likely goal.
- Hindsight optimization approximates the intractable continuous-state POMDP by minimizing expected cost-to-go without requiring single-goal prediction.The approximation is computationally efficient and does not oppose the user to gather information.
- Across teleoperation studies, the method achieved goals faster and with less joystick input than predict-then-act, while teaming reduced idling and collision time.In teaming, total completion-time results were mixed because predict-then-act could execute faster after confident goal prediction.
2 Related Works
Prior shared-autonomy systems combine user input with autonomy through takeovers, constraints, or blending, but many rely on predicting one goal before assisting. The policy formulation instead optimizes robot action given user input and expected costs.
- Shared-control systems include autonomous takeovers, high-level commands, minimal input augmentation, and constraint-based assistance.Examples include confidence-triggered autonomy, point-and-click or gaze interfaces, potential fields, virtual fixtures, and collision-avoidance controllers.
- Blending combines independently selected user and robot actions through an arbitration function that controls their relative contributions.Blending is widely used because of its computational efficiency, simplicity, and empirical effectiveness.
- Blending can fail catastrophically because independently successful decisions are combined without evaluating the action that will actually execute.
- The proposed approach differs from methods that predict a single goal before assisting by optimizing assistance for a distribution over goals.The paper also contrasts its method with prior expected-cost approaches that use simplifying assumptions such as squared-distance costs.
- Prior teaming methods model human motion or workspace occupancy to improve efficiency, avoid collisions, and incorporate collaboration constraints.These approaches include predicted human motion, safety and social acceptability constraints, and task constraints such as visibility and reachability.
- The policy method optimizes the robot action conditioned on the user action rather than treating the two actions as separate decisions.The user policy minimizes expected user costs, while the system policy minimizes expected costs conditioned on user input.
3 Framework
The framework models shared autonomy with an unknown, fixed user goal and selects assistance by minimizing expected cost-to-go over goal beliefs. Because exact POMDP optimization is intractable for continuous spaces, it uses hindsight optimization and approximations to compute actions efficiently.
- The framework models shared autonomy as minimizing a cost function when the user’s goal is unknown.
- Known-goal formulation: The environment state includes the user’s fixed intended goal, while transitions update the environment without changing that goal.
- Known-goal formulation: The robot observes user actions and selects actions through a policy conditioned on the state and user input.
- Unknown-goal formulation: The unknown-goal problem is formulated as a POMDP whose belief represents uncertainty over the user’s goal while the environment state remains known.
- Hindsight optimization: Hindsight optimization approximates the belief value by exchanging minimization and expectation, effectively assuming uncertainty resolves at the next timestep.
- Hindsight optimization: Hindsight optimization supports continuous state and action spaces and fast reaction, but performs poorly when explicit information gathering is necessary.
- User prediction: User goals are inferred from observed actions using a user policy modeled with maximum entropy inverse optimal control.
4 Shared Control Teleoperation
Across shared-control teleoperation tasks, the policy method improved objective performance and reduced user effort, while effects on preference and perceived control varied by task. In feeding, greater autonomy generally improved performance, but users retained benefits from limited control for fine alignment.
- 4.1 Grasping Experiment: The policy method enabled faster object grasping with less joystick input than the comparison method, supporting H1a and H1b.Trial time and total control input both showed significant main effects: F(1, 9) = 12.98, p = 0.006, and F(1, 9) = 7.76, p = 0.021, respectively.
- 4.1 Grasping Experiment: Users showed a nonsignificant preference trend toward blending, despite the policy method achieving faster grasping with less input.Preference ratings favored blend, with policy M = 4.10, SD = 1.64 versus blend M = 4.90, SD = 1.58; the difference was not statistically significant.
- Motivation: Eating independently is a difficult but important assistive-robotics task, motivating evaluation in a realistic quality-of-life domain.Users of assistive arms generally avoid eating tasks because they require too much effort.
- 4.2 Feeding Experiment: In feeding, autonomy produced faster task completion and less joystick input, with significant differences across the evaluated control methods.Execution time differed significantly, F(1.89, 39.73) = 43.55, p < 0.001; total joystick input also differed significantly, F(1.67, 35.14) = 65.35, p < 0.001.
- 4.2 Feeding Experiment: More autonomy reduced mode switches but also reduced users’ feelings of control, while improving several subjective usability and preference measures.Mode switches differed significantly, F(2.30, 48.39) = 65.16, p < 0.001, and control agreement decreased with autonomy; users liked and ranked the more autonomous shared-autonomy method higher.
- 4.2 Feeding Experiment: Policy retained a potential advantage over full autonomy because users could fine-tune end-effector alignment to compensate for bite-localization inaccuracies.Policy had a slightly higher, though nonsignificant, average completion ratio than full autonomy; the current policy continually opposed such corrective input.
5 Human-Robot Teaming
The teaming framework models user–robot goal dependencies conservatively and uses hindsight optimization for responsive assistance. In experiments, the policy reduced collisions and human idling, but increased robot and total task duration relative to faster methods.
- Task Model: The restriction set R excludes user–robot goal pairs that cannot be achieved simultaneously.This conservative model captures conflicts such as both agents needing the same object.
- Task Model: The user and robot jointly determine state transitions, with user and robot actions affecting their respective state portions.The transition function applies the two actions sequentially and deterministically.
- Hindsight Optimization: Hindsight optimization enables rapid responses to changing user goals in continuous state and action spaces.The method is presented as a suitable POMDP approximation for human–robot teaming.
- Results: 0.0% collision time was observed for policy versus 0.44% for plan, with the difference significant at p = 0.027.The policy never entered the 8cm collision threshold, whereas plan did.
- Results: 65.9s total task duration for policy was significantly slower than fixed at 58.6s and plan at 60.6s.The slower policy execution dominated total task time.
- Results: 0.46s policy idle time was significantly lower than 1.62s for fixed, while human task duration showed no significant method effect.Robot task duration was significantly affected by method, and policy robot execution was slower.
- Results: Participant ratings were significantly affected by collisions, but not by method alone.The analysis reported no interaction and did not directly support the method-preference hypothesis.
6 Discussion and Conclusion
The paper concludes that shared autonomy can assist over uncertain goal distributions rather than waiting for a single confident prediction. Across teleoperation and teaming, this improved several objective outcomes, while preferences and cost-model scope remained important limitations.
- Conclusion: The method assists over a distribution of user goals instead of predicting a single goal first.The motivation is that predict-then-act provided no assistance for 69% of execution time in the authors’ experiment.
- Conclusion: Hindsight optimization approximates the POMDP to provide real-time assistance over continuous state and action spaces.The framework was tested in two shared-control teleoperation scenarios and one teaming scenario.
- Teleoperation Findings: The method outperformed predict-then-act across all objective measures in both teleoperation tasks, although user preference differed by task.Users significantly preferred the shared autonomy method for eating but not significantly for grasping.
- Teleoperation Findings: Predict-then-act provided no assistance for the first 29.4 seconds on average in feeding, exceeding the shared autonomy method’s 18.5-second total average time.The confidence threshold was typically reached only after users completed difficult fork-orientation mode switches.
- Limitations: The simple distance-based cost function enabled closed-form value computation and assistance at 50 times per second.Its assistance objective was limited to minimizing time to reach a goal, rather than modeling user effort differences.
- Limitations: Users varied greatly in preferences and desires, motivating adaptation to individual user preferences.The discussion reports mixed preferences across tasks and high within-task response variance.
Appendix A Variable Definitions
The appendix provides a table of variable definitions for reference.
- Appendix A Variable Definitions: Variable definitions are provided in table 3.
- Appendix A Variable Definitions: The table serves as a reference for variables used in the paper.
- Appendix A Variable Definitions: No additional derivation or experimental result is stated in this appendix passage.
Appendix B Multi-Target MDPs Proofs
The appendix supplies proofs for decomposing value functions in MDPs with multiple targets.
- Appendix B Multi-Target MDPs Proofs: The appendix presents proofs for decomposing value functions in MDPs with multiple targets.
- Appendix B Multi-Target MDPs Proofs: The proofs concern MDPs with multiple targets introduced earlier in section 3.5.
- Appendix B Multi-Target MDPs Proofs: This passage identifies the appendix’s proof topic but does not state a specific theorem or result.
B.1 Theorem 1: Decomposing value functions
Theorem 1 shows that, for deterministic MDPs with deterministic user policies, value and action-value functions can be decomposed over targets. In the shared-autonomy setting, the resulting goal value equals the minimum target-specific cost-to-go.
- Theorem statement: The theorem applies to deterministic MDPs with deterministic user policies and decomposes value and action-value functions over targets.The proof is presented for shared autonomy, while the stated result applies to any deterministic MDP.
- Proof: The proof computes Qg and Vg backwards using standard value iteration, beginning at the final timestep T.
- Proof: At each recursive step, the analysis selects the target κ∗ with the lowest target-specific value at the next state.
- Result: For deterministic systems, the goal value satisfies Vg(x) ≤ minκ Vκ(x) and therefore equals minκ Vκ(x).Vκ(x) is the cost-to-go for a specific target, which bounds the total cost-to-go in this setting.
B.2 Theorem 2: Decomposing soft value functions
Theorem 2 addresses decomposition of soft value functions over targets for deterministic MDPs. Its proof uses additive trajectory costs, marginalization over future inputs and targets, and the trajectory probability definition.
- Theorem statement: Theorem 2 defines trajectory-target probability as p(ξ, κ) ∝ exp(−Cκ(ξ)) for deterministic MDPs.
- Theorem statement: The theorem concerns computing the soft value function for goal g from target-specific soft value functions.
- Proof: The proof exploits additive trajectory cost to expand exp(−Cκ(ξ)) and marginalize over future inputs to obtain the probability of the current input.
- Proof: The remaining derivation integrates over trajectories, marginalizes out κ, and simplifies using the definition of exp(−Ṽg,t(x, u)).