Where Should I Join? Robot Group Joining via Language-Guided Goal Prediction
A language-guided method predicts where a robot should join conversations, queues, and audiences.
The paper formulates language-grounded robot group joining: given an observation and a natural-language description, the robot identifies relevant members and predicts socially compliant joining poses. Candidate subsets are generated by recursive spectral partitioning and ranked with a language-conditioned image-geometry model, after which a goal predictor uses human-formation priors to score feasible poses. Experiments on conversations, queues, and audiences report competitive grounding accuracy, sub-second inference, and better joining-pose prediction than baselines, including real-robot tests in static and changing interactions.
- Robot infers which group to join from vision and a language description.
- Recursive spectral partitioning proposes candidate member subsets for ranking.
- Goal predictor outputs a multimodal energy-orientation map of joining poses.
- Real-robot trials covered static and dynamically changing group interactions.
From the original article
1 min read at arxiv.orgSocial navigation typically assumes a specified goal and focuses on reaching it while respecting social conventions, whereas robot group joining requires predicting where to join based on the group's real-time activity and formation. This is a highly semantic task, yet an important capability for applications such as robotic guide dogs and autonomous mobility scooters.
By Zilin Fang · arxiv.org
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