arXiv AI June 17, 2026 By Yuji Takubo, Maximilian Adang, Mac Schwager, Simone D'Amico

Transformer-Based Warm-Starting for Feasible and Optimal Terminal Approach to Tumbling Objects with Space Manipulators

arXiv:2606. 17317v1 Announce Type: cross Abstract: Real-time trajectory generation for on-orbit robotic servicing is challenging due to the nonlinear coupling between spacecraft bus motion, manipulator dynamics, visibility cone, and trajectory-level safety constraints.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 13

PAC-ACT: Post-training Actor-Critic for Action Chunking Transformers

arXiv:2607. 09590v1 Announce Type: cross Abstract: Precision industrial contact manipulation requires reliable robot policies under pose perturbations and contact-force constraints.

By Yujie Pang, Zudong Li

llms rag robotics fine-tuning efficiency multimodal benchmarks safety

arXiv AI

Jun 6

From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution

arXiv:2604. 12474v3 Announce Type: replace-cross Abstract: In many robotic tasks, agents must traverse a sequence of spatial regions to complete a mission.

By Lidor Erez, Shahaf S. Shperberg, Ayal Taitler

agents reinforcement-learning robotics

arXiv Machine Learning

Jul 1

Warp RL: Reshaping Base Policy Distributions for Dynamics Adaptation

arXiv:2606. 31043v1 Announce Type: new Abstract: Residual reinforcement learning adapts a pretrained robot policy by learning an additive correction to its actions.

By Ethan Hirschowitz, Fabio Ramos

reinforcement-learning robotics

arXiv AI

22h ago

LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models

arXiv:2605. 09948v2 Announce Type: replace Abstract: Current Vision-Language-Action (VLA) models typically treat the deepest representation of a vision-language backbone as universally optimal for action prediction.

By Boyang Shen, Kaixiang Yang, Hao Wang, Qiuyu Yu, Qiang Xie, Qiang Li, Zhiwei Wang

llms robotics efficiency multimodal safety

arXiv AI

Jun 6

Sample-efficient Low-level Motion Planning for Robotic Manipulation Tasks via Zero-shot Transfer Learning

arXiv:2606. 06041v1 Announce Type: cross Abstract: As robotic systems become more sophisticated, the growing complexity of their motion planning models and the longer training times pose substantial challenges.

By Yuanzhi He, Victor Romero-Cano, Jos\'e J. Pati\~no, Juan David Hern\'andez, William Sawtell, Gualtiero Colombo

robotics fine-tuning

arXiv AI

Jun 6

TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation

arXiv:2606. 06218v1 Announce Type: cross Abstract: A policy tuned for one robot often behaves differently on another, whether due to the sim-to-real gap, unknown payloads, or the differing dynamics of two instances of the same robot.

By Dongwon Son, Florian Shkurti, Jason Lee, Naman Shah, Beomjoon Kim, Dieter Fox

reinforcement-learning robotics fine-tuning

Related stories

PAC-ACT: Post-training Actor-Critic for Action Chunking Transformers

From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution

Warp RL: Reshaping Base Policy Distributions for Dynamics Adaptation

LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models

Sample-efficient Low-level Motion Planning for Robotic Manipulation Tasks via Zero-shot Transfer Learning

TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation