# CoTaP: Compliant Task Pipeline and Reinforcement Learning of Its Controller with Compliance Modulation

arXiv: https://arxiv.org/abs/2509.25443 · HTML: https://mlsanity.com/paper/2509.25443/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 16 of 16)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [Perpetual Humanoid Control for Real-time Simulated Avatars](https://mlsanity.com/paper/2305.06456/) (2023)
- [HumanPlus: Humanoid Shadowing and Imitation from Humans](https://mlsanity.com/paper/2406.10454/) (2024)
- [Expressive Whole-Body Control for Humanoid Robots](https://mlsanity.com/paper/2402.16796/) (2024)
- [Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation](https://mlsanity.com/paper/2403.04436/) (2024)
- [TWIST: Teleoperated Whole-Body Imitation System](https://mlsanity.com/paper/2505.02833/) (2025)
- [Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning](https://mlsanity.com/paper/2501.02116/) (2025)
- [FALCON: Learning Force-Adaptive Humanoid Loco-Manipulation](https://mlsanity.com/paper/2505.06776/) (2025)
- [HumanMimic: Learning Natural Locomotion and Transitions for Humanoid Robot via Wasserstein Adversarial Imitation](https://mlsanity.com/paper/2309.14225/) (2023)
- [Whole-body Humanoid Robot Locomotion with Human Reference](https://mlsanity.com/paper/2402.18294/) (2024)
- [FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots](https://mlsanity.com/paper/2505.06883/) (2025)
- [JAEGER: Dual-Level Humanoid Whole-Body Controller](https://mlsanity.com/paper/2505.06584/) (2025)
- [Learning Quiet Walking for a Small Home Robot](https://mlsanity.com/paper/2502.10983/) (2025)
- [Variable Stiffness for Robust Locomotion through Reinforcement Learning](https://mlsanity.com/paper/2502.09436/) (2025)

## Built on by (most cited first, 1 of 1)

- [Task-Specified Compliance Bounds for Humanoids via Lipschitz-Constrained Policies](https://mlsanity.com/paper/2603.16180/) (2026)

## Abstract (arXiv)

Humanoid whole-body locomotion control is a critical approach for humanoid robots to leverage their inherent advantages. Learning-based control methods derived from retargeted human motion data provide an effective means of addressing this issue. However, because most current human datasets lack measured force data, and learning-based robot control is largely position-based, achieving appropriate compliance during interaction with real environments remains challenging. This paper presents Compliant Task Pipeline (CoTaP): a pipeline that leverages compliance information in the learning-based structure of humanoid robots. A two-stage dual-agent reinforcement learning framework combined with model-based compliance control for humanoid robots is proposed. In the training process, first a base policy with a position-based controller is trained; then in the distillation, the upper-body policy is combined with model-based compliance control, and the lower-body agent is guided by the base policy. In the upper-body control, adjustable task-space compliance can be specified and integrated with other controllers through compliance modulation on the symmetric positive definite (SPD) manifold, ensuring system stability. We validated the feasibility of the proposed strategy in simulation, primarily comparing the responses to external disturbances under different compliance settings.