Scaling SoftAct to Longer-Horizon, Multi-Stage Manipulation

Develop an extension of the SoftAct contact- and force-aware retargeting and policy learning framework that can handle longer-horizon, multi-stage manipulation tasks in which contact modes and the functional roles of contacts evolve over time, while maintaining reliable performance with a non-anthropomorphic pneumatic soft robot hand.

Background

SoftAct transfers human manipulation skills to a non-anthropomorphic soft robot hand by explicitly leveraging contact geometry and force distributions instead of kinematic correspondence. The presented experiments demonstrate effectiveness across several contact-rich tasks but focus on relatively short-horizon behaviors with fixed interaction structure.

The authors note that many real-world manipulations are composed of multiple stages where contact modes and the functional roles of contacts change over time. Extending SoftAct to reliably operate in such longer-horizon, evolving-contact scenarios is identified as an unresolved challenge, motivating research into temporal abstraction, contact-mode reasoning, and robust policy execution over extended horizons.