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Learning to Plan in Human-Robot Collaboration: Multimodal Reinforcement Learning for Adaptive Interaction
Afagh Mehri Shervedani, Siyu Li, Natawut Monaikul, Bahareh Abbasi, Barbara Di Eugenio, Miloš Žefran
TL;DR
Handcrafted multimodal interaction policies are difficult to scale across tasks, motivating a learned alternative for assistive robots. The paper trains an RL interaction manager with a human-data-based simulator and evaluates it in a human study. The system reports 96.0% task success and 90.2% overall system accuracy, with high participant ratings on several measures.
Problem
Handcrafted multimodal interaction policies need redesign for each new task, while the interaction-data domain is sparse.
Source: §I · p. 1Method
The paper combines an interpretable RL interaction manager with a neural-network user simulator trained on human interaction data and augmented with synthetic misunderstandings.
Source: §I · p. 1Results
The system achieved a 96.0% task success rate and 90.2% overall system accuracy across 469 human inputs.
Source: C. Results · p. 3Takeaways & Limitations
The results demonstrate the framework’s potential for multimodal human–robot interaction, including socially assistive applications.
Source: A. Limitations, Applications, and… · p. 4The HBATN comparison was limited by a study involving 12 participants.
Source: A. Limitations, Applications, and… · p. 4
Abstract
from arXiv · showhide
Robot assistants for older adults and people with disabilities need to perform collaborative tasks with users effectively. The core component of these systems is an interaction manager whose job is to observe and assess the task and infer the state of the human and their intent for the robot to choose the best course of action. Due to the sparseness of the data in this domain, the policy for such multimodal systems is often crafted by hand; as the complexity of interactions grows, this process is not scalable. This paper proposes a reinforcement learning (RL) approach to automatically generate the multimodal policy of the robot. Our system focuses on a realistic scenario where a robot assists a user in locating objects within a home environment, managing multimodal signals, including language and physical actions, to select the best action. In contrast to traditional dialog systems, our agent is trained with a simulator that uses human data and can deal with multiple modalities. We use a simple high-level reward function that needs no fine-tuning and enforce some preconditions to speed up the training process. A human study evaluating the system in a real-world setting demonstrates promising results, indicating high usability and effective task completion. This RL-based approach offers a scalable and interpretable alternative for designing interaction managers in multimodal human-robot collaborations.
I. INTRODUCTION
Assistive robots must interpret multimodal user behavior and select actions through an interaction manager, but manually built policies must be redesigned for each task. This paper proposes an interpretable RL interaction manager and human-data-trained simulator for multimodal collaboration.
I. INTRODUCTION
- The assistive-robot pipeline interprets the environment and user behavior, selects a response, and executes that action.These stages are perception, interaction management, and execution, respectively.Source: §I · p. 1
- Manual HBATN interaction policies worked for the Find task but would need redesign for each new task.The Find task involves collaboratively locating a hidden object using speech, pointing gestures, and haptic-ostensive actions.Source: §I · p. 1
- The proposed system learns an interpretable multimodal interaction policy with RL and trains the robot to act as HEL alongside a human in the ELD role.The evaluation includes a human-subject study and assesses the interaction manager and perception and execution components.Source: §I · p. 1
- A neural-network user simulator trained on Find-task data models human responses and adds synthetic data-driven misunderstandings and interaction errors.The error-injection module exposes the RL agent to imperfect interactions during training.Source: §I · p. 1
II. REINFORCEMENT LEARNING FRAMEWORK
The HEL agent learns by interacting with a neural-network user simulator, with its state tracking agreement between the user and robot about the target object and its location. DAGGER warm-up initializes DQN training using expert demonstrations.
II. REINFORCEMENT LEARNING FRAMEWORK
- The HEL agent receives the simulator’s action and previous state, then outputs a Dialogue Act and physical action.Its state tracks whether target-object type, potential location, and target object are unknown, match, or mismatch relative to the user’s state.Source: §II · pp. 1–2
A. Model Architecture and Warm-up
- A lightweight fully connected network was more effective and computationally efficient than evaluated BERT, ALBERT, DistilBERT, and Transformer-XL alternatives on the small interaction dataset.The selected network has two fully connected layers, dropout of 0.1, and a ReLU output layer.Source: A. Model Architecture and Warm-up · p. 2
- DQN training is initialized with a DAGGER policy after 10 episodes, before the policy overfits to the limited demonstrations.During DAGGER, expert actions from ELDERLY-AT-HOME are recorded as corrections but not executed; training runs for 25 episodes of at most 25 turns.Source: A. Model Architecture and Warm-up · p. 2, §II · p. 2
- A 25% error-injection rate converts applicable MATCH states to MISMATCH to expose the agent to mismatches rare in the original corpus.The DQN policy and target networks are initialized from the DAGGER policy.Source: A. Model Architecture and Warm-up · p. 2, §II · p. 2
B. Deep-Q-Learning
DAGGER warm-up improves DQN training relative to training from scratch. The reward encourages efficient completion while penalizing unsuccessful or precondition-violating actions.
B. Deep-Q-Learning
- DAGGER warm-up supplies a reasonable initial policy and reduces the need to encode complex human behavior in the reward function.Both the DQN policy and target networks are initialized with DAGGER weights.Source: B. Deep-Q-Learning · p. 2
- The reward gives +2r for success, −2r for reaching the 20-turn limit unsuccessfully, −r per action, and −Z for violating task preconditions.Here r = 1 and Z = 50; verification is disallowed until the necessary user-provided information is available.Source: B. Deep-Q-Learning · p. 2
- Training stops before performance degrades after approximately 6,000 episodes, when replay memory begins replacing older samples.The policy network is optimized every 500 episodes and copied to the target network every 2,000 episodes.Source: B. Deep-Q-Learning · p. 2
- After 10,000 episodes, DQN trained without warm-up still does not reach the initial performance of the DAGGER-initialized agent.The warmed-up agent converges faster, achieves higher rewards and success rates, and stabilizes earlier than DQN trained from scratch.Source: B. Deep-Q-Learning · pp. 2–3
A. Robot Implementation
The Baxter implementation converts speech and gestures into the interaction manager’s inputs and executes its outputs as speech and physical actions. In a 12-person study, the system achieved high task success, while repeated trials significantly increased system accuracy but not task-level efficiency or success.
A. Robot Implementation
- Perception extracts dialogue acts, objects, and locations from speech and pointing; execution turns agent outputs into synthesized speech and Baxter actions.The dialogue-act classifier achieved 88.41% test accuracy on 7,617 ELDERLY-AT-HOME datapoints.Source: A. Robot Implementation · p. 3
B. User Study
- Twelve healthy adults completed 75 trials, collaborating with the robot to locate a target object placed among a drawer, shelf, and cabinet.Participants completed 5–7 trials each, with colored cups and balls randomly placed across locations.Source: B. User Study · p. 3
C. Results
- The system achieved a 96.0% task success rate and 90.2% overall system accuracy; with correctly perceived input, the interaction manager reached approximately 97.8% accuracy.Component-level error rates were 6.4% for serious speech recognition errors, 15.1% for incorrect dialogue acts, and 13.6% for incorrect pointing gestures; 15.2% of non-eligible responses were attributable to the RL policy.Source: C. Results · p. 3
- System accuracy significantly increased from 86.6% to 95.1% across repeated trials, while Average Turns and Success Rate showed no significant differences.The reported p-values were 0.036 for System Accuracy, 0.378 for Average Turns, and 0.655 for Success Rate.Source: C. Results · p. 3
D. Comparison with HBATNs
Compared with the HBATN-based system, the RL-based system improved interaction length and success rate while keeping dialogue-act classification and interaction-manager accuracy comparable. Participants reported positive survey scores, but repeated trials did not significantly change task-level efficiency or success.
D. Comparison with HBATNs
- SSREs fell from 17.3% to 6.4% and incorrect pointing from 28.9% to 13.6%, while DA classification and Interaction Manager accuracy remained comparable.The SSRE reduction was statistically significant (p = 0.008).Source: D. Comparison with HBATNs · p. 3
- Average interaction length fell from 15.6 to 12.6 moves and success rose from 85.7% to 96.0% versus HBATNs.The reported improvements were statistically significant (p = 0.017 and p = 0.010, respectively).Source: D. Comparison with HBATNs · p. 3, Table II · p. 4
IV. DISCUSSION
- Survey scores were 6.03/7 for Responsiveness, 5.73/7 for Comfort, 4.33/5 for Familiarity and Learning, and 4.25/5 for Overall Quality; Fatigue and Habituation scored 1.69/5.Cronbach’s alpha values for the multi-item groups ranged from 0.71 to 0.82.Source: Table III · p. 4, §IV · p. 4
- Participants appeared to adapt over repeated trials, but Average Turns and Success Rate did not change significantly; only System Accuracy differed significantly between early and later trials.The discussion suggests participants may have adapted their interaction strategies without substantially affecting task-level performance.Source: §IV · p. 4
- Overall interaction quality was 4.25/5 for the RL system versus 4/5 for HBATNs, with no statistically significant difference (p = 0.32).The comparison was limited to Overall Quality because the HBATN study reported no other survey measure or subject-level responses.Source: §IV · p. 4
A. Limitations, Applications, and Future Work
The paper identifies limits in comparing against the HBATN system and in extending the framework to new tasks. It also describes potential applications and future directions for multimodal assistive interaction.
A. Limitations, Applications, and Future Work
- The HBATN comparison relies partly on aggregate values and an assumed uniform distribution, which may reduce statistical precision and warrants caution interpreting improvements.The prior study had 7 participants versus 12 in this study, and its subject-level data were unavailable.Source: A. Limitations, Applications, and… · p. 4
- The framework could be applied to healthcare assistance, education, and other settings requiring adaptive interaction.The paper highlights socially assistive applications where robots interpret verbal and non-verbal user actions.Source: A. Limitations, Applications, and… · p. 4
- Each new task requires a suitable user simulator, multimodal demonstration data, and substantial development effort.The paper proposes data-efficient task-specific simulators and generic simulators for multiple HRI tasks as future work.Source: A. Limitations, Applications, and… · p. 4
- Combining Behavioral Cloning with LLMs is proposed as a way to pair general language knowledge with task-specific demonstrations that ground simulator behavior.The authors identify this combination as a promising direction.Source: A. Limitations, Applications, and… · p. 4
- The authors plan to extend the framework to physical HRI, where robots must reason about human intent and physical interaction.Force and torque sensing and transfer learning are proposed to support responses to physical cues and adaptation to new pHRI tasks.Source: A. Limitations, Applications, and… · p. 4
V. CONCLUSION
The paper presents an RL framework for multimodal interaction managers in assistive robotics and reports promising human-study results. It concludes that the approach offers a scalable way to build interaction managers for multimodal collaboration.
V. CONCLUSION
- The conclusion reports advantages over the HBATN-based approach in speech recognition accuracy, DA classification performance, and overall system quality.The human study also found high user satisfaction through positive feedback.Source: §V · p. 5
- The proposed RL framework targets interaction managers for multimodal collaborative tasks involving older adults and individuals with disabilities.Source: §V · p. 5
- The authors characterize the RL approach as a scalable solution for interaction managers that can adapt to different tasks and environments.They identify further refinement, additional applications, and generalization to other tasks as future directions.Source: §V · p. 5
APPENDIX A THE SURVEY QUESTIONS
The appendix lists survey questions about participants’ experience with the robot. They cover perceived interaction quality, comfort and acceptability, learning and adaptation, fatigue, and expectations.
APPENDIX A THE SURVEY QUESTIONS
- Questions assess confidence in the robot, predictability of its behavior, responsiveness, expression of intent, and initiative when instructions are unclear.Source: Appendix A · p. 6
- Questions ask about comfort, willingness to accept the robot as an assistant when locations are reachable, perceived similarity to a human assistant, and whether interaction felt natural and relaxed.Source: Appendix A · p. 6
- Participants are asked whether interacting with the robot became easier over trials and whether they adapted their behavior.Source: Appendix A · p. 6
- Questions measure tiredness, frustration, and boredom as trials continue.Source: Appendix A · p. 6
- A question asks participants to rate their experience against their expectations.Source: Appendix A · p. 6