arXiv Machine Learning
A Robust Task-Level Control Architecture for Learned Dynamical Systems
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A context-adaptive policy framework for robust and reactive robotic manipulation via uncertainty-aware imitation learning
arXiv:2410. 24035v2 Announce Type: replace-cross Abstract: Generating robust and reactive manipulation strategies that can adapt to changing context information is a challenging task in robotics.
arXiv Machine Learning
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TEMPO: Learning Temporal Context for Dynamic Robot Manipulation
arXiv:2609.16864v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have achieved impressive performance in quasi-static manipulation, but struggle in dynamic manipulation tasks bec...
arXiv AI
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ORPA: Online Residual Policy Adaptation for Robot Manipulation Control with Human Feedback
The paper introduces ORPA, a framework that adds a lightweight, feedback-conditioned module to a pretrained robotic manipulation policy, enabling real‑time residual adjustments in joint space without retraining the base policy. ORPA allows immediate correction of execution errors and distribution shifts, improving success rates and recovery on precision‑sensitive tasks compared to baseline policies and rule‑based inverse kinematics. The method is evaluated on the ALOHA platform, showing its effectiveness in real‑time deployment scenarios.
arXiv Machine Learning
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Dynin-Robotics: Omnimodal Unified Diffusion Vision-Language-Action Model
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arXiv Machine Learning
Jun 10
GHOST: Hierarchical Sub-Goal Policies for Generalizing Robot Manipulation
arXiv:2606. 10025v1 Announce Type: cross Abstract: We present GHOST, a framework for learning visuomotor manipulation policies that generalize beyond the training distribution.
arXiv AI
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Sensitivity Shaping for Latent Modeling
arXiv:2606. 14585v1 Announce Type: cross Abstract: Generative dynamics models enable planning in challenging robotic systems, but safe deployment requires reliably detecting policy-induced out-of-distribution (OOD) transitions.