Bidirectional Human-Robot Communication for Physical Human-Robot Interaction
- URL: http://arxiv.org/abs/2601.10796v1
- Date: Thu, 15 Jan 2026 19:01:02 GMT
- Title: Bidirectional Human-Robot Communication for Physical Human-Robot Interaction
- Abstract summary: BRIDGE is a system for bidirectional human-robot communication in physical assistance.<n>Our method allows users to modify a robot's planned trajectory in real time using natural language.<n>Our system provides verbal feedback in response to the user, either assuring any resulting changes or posing a clarifying question.
- Score: 13.222878560524896
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Effective physical human-robot interaction requires systems that are not only adaptable to user preferences but also transparent about their actions. This paper introduces BRIDGE, a system for bidirectional human-robot communication in physical assistance. Our method allows users to modify a robot's planned trajectory -- position, velocity, and force -- in real time using natural language. We utilize a large language model (LLM) to interpret any trajectory modifications implied by user commands in the context of the planned motion and conversation history. Importantly, our system provides verbal feedback in response to the user, either assuring any resulting changes or posing a clarifying question. We evaluated our method in a user study with 18 older adults across three assistive tasks, comparing BRIDGE to an ablation without verbal feedback and a baseline. Results show that participants successfully used the system to modify trajectories in real time. Moreover, the bidirectional feedback led to significantly higher ratings of interactivity and transparency, demonstrating that the robot's verbal response is critical for a more intuitive user experience. Videos and code can be found on our project website: https://bidir-comm.github.io/
Related papers
- Human-Human & Human-Robot Interaction Transformer (H2INT) for Robot Navigation in Dense and Uncertain Crowds [22.53637772629958]
This paper presents a reinforcement learning framework that retains robot-conditioned changes in pedestrian motion during policy learning.<n> Responsiveness affects the crowd dynamics when the robot is visible but is not supplied as a policy input.<n>A curriculum gradually reduces pedestrian responsiveness to increase interaction difficulty.
arXiv Detail & Related papers (2026-09-04T15:53:56Z) - RoboOmni: Proactive Robot Manipulation in Omni-modal Context [165.09049429566238]
We introduce cross-modal contextual instructions, a new setting where intent is derived from spoken dialogue, environmental sounds, and visual cues rather than explicit commands.<n>We present RoboOmni, a framework based on end-to-end omni-modal LLMs that unifies intention recognition, interaction confirmation, and action execution.<n>Experiments in simulation and real-world settings show that Robo Omni surpasses text- and ASR-based baselines in success rate, inference speed, intention recognition, and proactive assistance.
arXiv Detail & Related papers (2025-10-27T18:49:03Z) - Learning Manipulation by Predicting Interaction [85.57297574510507]
We propose a general pre-training pipeline that learns Manipulation by Predicting the Interaction.
The experimental results demonstrate that MPI exhibits remarkable improvement by 10% to 64% compared with previous state-of-the-art in real-world robot platforms.
arXiv Detail & Related papers (2024-06-01T13:28:31Z) - Learning to Communicate Functional States with Nonverbal Expressions for Improved Human-Robot Collaboration [3.5408317027307055]
Collaborative robots must effectively communicate their internal state to humans to enable a smooth interaction.
We propose a reinforcement learning algorithm based on noisy human feedback to produce accurately interpreted nonverbal auditory expressions.
arXiv Detail & Related papers (2024-04-30T04:18:21Z) - Real-time Addressee Estimation: Deployment of a Deep-Learning Model on
the iCub Robot [52.277579221741746]
Addressee Estimation is a skill essential for social robots to interact smoothly with humans.
Inspired by human perceptual skills, a deep-learning model for Addressee Estimation is designed, trained, and deployed on an iCub robot.
The study presents the procedure of such implementation and the performance of the model deployed in real-time human-robot interaction.
arXiv Detail & Related papers (2023-11-09T13:01:21Z) - A Graph-to-Text Approach to Knowledge-Grounded Response Generation in Human-Robot Interaction [2.098944952054856]
This paper presents a novel conversational model for human--robot interaction that rests upon a graph-based representation of the dialogue state.<n>The neural conversational model employed to respond to user utterances relies on a simple but effective graph-to-text mechanism.<n>The proposed approach is empirically evaluated through a user study with a humanoid robot.
arXiv Detail & Related papers (2023-11-03T15:44:28Z) - Incremental Learning of Humanoid Robot Behavior from Natural Interaction and Large Language Models [23.945922720555146]
We propose a system to achieve incremental learning of complex behavior from natural interaction.
We integrate the system in the robot cognitive architecture of the humanoid robot ARMAR-6.
arXiv Detail & Related papers (2023-09-08T13:29:05Z) - "No, to the Right" -- Online Language Corrections for Robotic
Manipulation via Shared Autonomy [70.45420918526926]
We present LILAC, a framework for incorporating and adapting to natural language corrections online during execution.
Instead of discrete turn-taking between a human and robot, LILAC splits agency between the human and robot.
We show that our corrections-aware approach obtains higher task completion rates, and is subjectively preferred by users.
arXiv Detail & Related papers (2023-01-06T15:03:27Z) - Model Predictive Control for Fluid Human-to-Robot Handovers [50.72520769938633]
Planning motions that take human comfort into account is not a part of the human-robot handover process.
We propose to generate smooth motions via an efficient model-predictive control framework.
We conduct human-to-robot handover experiments on a diverse set of objects with several users.
arXiv Detail & Related papers (2022-03-31T23:08:20Z) - Synthesis and Execution of Communicative Robotic Movements with
Generative Adversarial Networks [59.098560311521034]
We focus on how to transfer on two different robotic platforms the same kinematics modulation that humans adopt when manipulating delicate objects.
We choose to modulate the velocity profile adopted by the robots' end-effector, inspired by what humans do when transporting objects with different characteristics.
We exploit a novel Generative Adversarial Network architecture, trained with human kinematics examples, to generalize over them and generate new and meaningful velocity profiles.
arXiv Detail & Related papers (2022-03-29T15:03:05Z) - Talk-to-Resolve: Combining scene understanding and spatial dialogue to
resolve granular task ambiguity for a collocated robot [15.408128612723882]
The utility of collocating robots largely depends on the easy and intuitive interaction mechanism with the human.
We present a system called Talk-to-Resolve (TTR) that enables a robot to initiate a coherent dialogue exchange with the instructor.
Our system can identify the stalemate and resolve them with appropriate dialogue exchange with 82% accuracy.
arXiv Detail & Related papers (2021-11-22T10:42:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.