arXiv AI
Estimating the Causal Impact of Social Navigation Signals in Corridor Scenarios
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arXiv Machine Learning
1d ago
Structured Machine Theory of Mind from Agent Trajectories
arXiv:2611.07208v1 Announce Type: new Abstract: Predictive models of human behavior trained on large-scale trajectory data optimize for statistical accuracy without representing the mental states tha...
arXiv AI
Sep 15
IMPACT-VLA: Interaction-aware Multimodal Propagation Attribution via Counterfactual Trajectories for Vision-Language-Action Policies
arXiv:2609.15005v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) policies perform robot manipulation tasks using multimodal inputs such as visual observations, proprioceptive states, an...
arXiv AI
Sep 15
Rethinking the Implications of Human Feedback for Preference Learning in Human-Robot Collaboration
The paper critiques the standard fixed-rule approach for deriving labels from human feedback in human-robot collaboration, showing that human-provided implication labels often differ and improve reward learning. It introduces IMPLIED, a method that starts with fixed-rule implications but learns to infer and revise accepted/rejected action labels over time, outperforming both the fixed rule and LLM baselines on recorded trajectories and a physical pizza‑making study. As a result, IMPLIED reduces preference‑estimation error and yields robot actions that better align with combined reward objectives.
arXiv AI
Jun 9
Causal Agent Replay: Counterfactual Attribution for LLM-Agent Failures
arXiv:2606. 08275v1 Announce Type: cross Abstract: When an LLM agent fails -- issues a refund it should not have, calls the wrong tool, leaks data -- existing tooling answers what happened (observability) or whether it passed (evaluation), but not which step caused the failure.
arXiv Computer Vision
Oct 7
OpenWAM: An Open Framework for Composable World-Action Models
arXiv:2610.07922v1 Announce Type: cross Abstract: World-action models (WAMs) couple future prediction with robot control, yet existing systems often vary the video backbone, interaction structure, su...
arXiv AI
Jul 17
Interventional Causal Circuits for Safe Robot Action Testing and Failure Recovery
arXiv:2607. 14826v1 Announce Type: cross Abstract: Safe physical AI for robot actions are required not only likely to succeed but tested to be safe before execution.