# Planning for Multi-stage Forceful Manipulation

arXiv: https://arxiv.org/abs/2101.02679 · HTML: https://mlsanity.com/paper/2101.02679/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2021.

## Built on by (most cited first, 5 of 5)

- [Long-Horizon Manipulation of Unknown Objects via Task and Motion Planning with Estimated Affordances](https://mlsanity.com/paper/2108.04145/) (2021)
- [Verifiably Following Complex Robot Instructions with Foundation Models](https://mlsanity.com/paper/2402.11498/) (2024)
- [Learning Ultrasound Scanning Skills from Human Demonstrations](https://mlsanity.com/paper/2111.09739/) (2021)
- [Task and Skill Planning: Hierarchical Robot Planning with Black-Box Skills](https://mlsanity.com/paper/2504.17901/) (2025)
- [IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation](https://mlsanity.com/paper/2606.10818/) (2026)

## Abstract (arXiv)

Multi-stage forceful manipulation tasks, such as twisting a nut on a bolt, require reasoning over interlocking constraints over discrete as well as continuous choices. The robot must choose a sequence of discrete actions, or strategy, such as whether to pick up an object, and the continuous parameters of each of those actions, such as how to grasp the object. In forceful manipulation tasks, the force requirements substantially impact the choices of both strategy and parameters. To enable planning and executing forceful manipulation, we augment an existing task and motion planner with controllers that exert wrenches and constraints that explicitly consider torque and frictional limits. In two domains, opening a childproof bottle and twisting a nut, we demonstrate how the system considers a combinatorial number of strategies and how choosing actions that are robust to parameter variations impacts the choice of strategy.