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arXiv2026

When Absolute State Fails: Evaluating Proprioceptive Encodings for Robust Manipulation

As end-to-end robotic policies are progressively deployed in the real world to solve real tasks, they face a gap between the training and inference conditions. Scaling the amount and diversity of the training data has shown some success in improving zero-shot generalization, yet robots still fail when faced with new, unseen test conditions. For instance, while robots with fixed frames of reference are common, those with moving frames pose a greater challenge for deployment. To address this specific instance of the issue, we present a study of strategies for encoding the robot's proprioceptive state to improve both in- and out-of-distribution performance at test time. Through a systematic study of joint representations, we find that a simple episode-wise relative frame provides the best trade-off between task performance and robustness, outperforming the baselines in extensive real-robot experiments conducted in a realistic test environment. The results suggest a practical path to leveraging data collected by robots with varying frames of reference and deployment to unseen test configurations.

cs.RO↗

arXiv2025

FTACT: Force Torque aware Action Chunking Transformer for Pick-and-Reorient Bottle Task

Manipulator robots are increasingly being deployed in retail environments, yet contact rich edge cases still trigger costly human teleoperation. A prominent example is upright lying beverage bottles, where purely visual cues are often insufficient to resolve subtle contact events required for precise manipulation. We present a multimodal Imitation Learning policy that augments the Action Chunking Transformer with force and torque sensing, enabling end-to-end learning over images, joint states, and forces and torques. Deployed on Ghost, single-arm platform by Telexistence Inc, our approach improves Pick-and-Reorient bottle task by detecting and exploiting contact transitions during pressing and placement. Hardware experiments demonstrate greater task success compared to baseline matching the observation space of ACT as an ablation and experiments indicate that force and torque signals are beneficial in the press and place phases where visual observability is limited, supporting the use of interaction forces as a complementary modality for contact rich skills. The results suggest a practical path to scaling retail manipulation by combining modern imitation learning architectures with lightweight force and torque sensing.

cs.RO↗