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

arXiv: https://arxiv.org/abs/2509.23112 · HTML: https://mlsanity.com/paper/2509.23112/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- First posted on arXiv 27 Sep 2025.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Built on by (most cited first, 4 of 4)

- [Rewind-IL: Online Failure Detection and State Respawning for Imitation Learning](https://mlsanity.com/paper/2604.16683/) (2026)
- [When Absolute State Fails: Evaluating Proprioceptive Encodings for Robust Manipulation](https://mlsanity.com/paper/2605.13067/) (2026)
- [Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning](https://mlsanity.com/paper/2608.07558/) (2026)
- [Generative Action-Chunk Sampling for Adaptive Stiffness Control in Physical Human-Robot Collaboration](https://mlsanity.com/paper/2608.25284/) (2026)

## Abstract (arXiv)

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.