Interaction-Aware Whole-Body Control for Compliant Object Transport

Hao Zhang; Yves Tseng; Ding Zhao; H. Eric Tseng

cs.ROMar 4, 2026

Interaction-Aware Whole-Body Control for Compliant Object Transport

Authors: Hao Zhang, Yves Tseng, Ding Zhao, H. Eric Tseng

Organizations: Department of Electrical Engineering, the University of Texas at Arlington, 76010 Arlington, USA · Department of Mechanical Engineering, Carnegie Mellon University, 15213 Pittsburgh, USA

Abstract

Cooperative object transport in unstructured environments remains challenging for assistive humanoids because strong, time-varying interaction forces can make tracking-centric whole-body control unreliable, especially in close-contact support tasks. This paper proposes a bio-inspired, interaction-oriented whole-body control (IO-WBC) that functions as an artificial cerebellum - an adaptive motor agent that translates upstream (skill-level) commands into stable, physically consistent whole-body behavior under contact. This work structurally separates upper-body interaction execution from lower-body support control, enabling the robot to maintain balance while shaping force exchange in a tightly coupled robot-object system. A trajectory-optimized reference generator (RG) provides a kinematic prior, while a reinforcement learning (RL) policy governs body responses under heavy-load interactions and disturbances. The policy is trained in simulation with randomized payload mass/inertia and external perturbations, and deployed via asymmetric teacher-student distillation so that the student relies only on proprioceptive histories at runtime. Extensive experiments demonstrate that IO-WBC maintains stable whole-body behavior and physical interaction even when precise velocity tracking becomes infeasible, enabling compliant object transport across a wide range of scenarios.

Figures & tables

Fig. 1 : The proposed IO-WBC architecture that bridges skill-level HRC commands with low-level execution through a kinematic-prior-based RG, enabling stable coordination between upper-body interaction and lower-body support under strong robot–object coupling.

Fig. 2 : The comprehensive learning and execution pipeline of IO-WBC. The RG is trained via supervised learning to provide kinematic priors. The interaction-oriented WBC policy is developed through teacher-student distillation, where a privileged teacher guides a student policy to decode interaction dynamics from proprioceptive history.

Fig. 3 : Comprehensive performance comparison across algorithmic variants. The first two panels report the norm errors in the lifting and pushing scenarios, while the two rightmost panels compare the load capacity and success rates.

Parameter	Value / Range
RG layers (hidden)	[128, 64]
IO-WBC policy layers (hidden)	[512, 256, 128]
Proprioceptive history ( $H_{t}$ )	25 steps
Control frequency	50 Hz
Discount factor ( $\gamma$ )	0.99
GAE factor ( $\lambda$ )	0.95

TABLE I : Detailed hyperparameters for RL training and curriculum specifications.

Fig. 4 : Visualization of the learning hierarchy in simulation. The framework encompasses single-agent skill learning for the IO-WBC layer and MARL for high-level HRC coordination.

Fig. 5 : Real-world deployment of IO-WBC on the HRC tasks between human and Unitree G1, including path-constrained collaborative pushing and lifting, as well as collaborative super-heavy object lifting. Specifically, the red dashed regions highlight the adaptive vertical synchronization and postural resilience during the transport of a 7 kg box and an 18 kg tire.

Fig. 6 : Tracking errors vs. lifting mass. IO-WBC maintains near-constant performance, while baselines exhibit exponential error growth in

E_{\alpha},E_{h},E_{v},

and

E_{\psi}

Mass	Algorithm	$\mathbf{E_{\alpha}}$ ( $\downarrow$ )	$\mathbf{E_{h}}$ ( $\downarrow$ )	$\mathbf{E_{v}}$ ( $\downarrow$ )	$\mathbf{E_{\psi}}$ ( $\downarrow$ )
0 kg	IO-WBC	0.125	0.015	0.046	0.134
	WBC	0.126	0.015	0.047	0.135
	w/o RG	0.188	0.051	0.048	0.403
	w/o Distill.	0.184	0.023	0.177	0.118
4 kg	IO-WBC	0.132	0.016	0.048	0.139
	WBC	0.166	0.034	0.098	0.158

TABLE II : Detailed comparison of HRC command tracking errors under cooperative lifting (0 kg–8 kg).

Mass	Algorithm	$\mathbf{E_{\alpha}}$ ( $\downarrow$ )	$\mathbf{E_{h}}$ ( $\downarrow$ )	$\mathbf{E_{v}}$ ( $\downarrow$ )	$\mathbf{E_{\psi}}$ ( $\downarrow$ )
0 kg	IO-WBC	0.125	0.015	0.046	0.134
	WBC	0.126	0.015	0.047	0.135
	w/o RG	0.286	0.049	0.048	0.413
	w/o Distill.	0.184	0.023	0.177	0.118
30 kg	IO-WBC	0.131	0.016	0.225	0.143
	WBC	0.211	0.055	0.356	0.246

TABLE III : Detailed comparison of HRC command tracking errors under cooperative pushing (0 kg–60 kg).

Fig. 7 : Tracking errors vs. pushing mass. Despite rising velocity errors (

E_{v}

), IO-WBC preserves high-fidelity postural control (

E_{\alpha},E_{h}

) up to 60 kg.

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RL for Robotics Humanoid Robot Control Whole-Body Control Impedance Control

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Whole-body compliant control is essential for deploying heavy humanoids under high payload in human-centric environments. Most prior force-aware learning-based pipelines focus on end-effector resistance, per-link upper-body springs, or end-effector stiffness modulation, leaving arbitrary-site perturbations on heavy platforms with lower-body engagement largely unaddressed. We close this gap with CompliantWBC comprising: (1) A base policy trained with RL to maximize compliance-fidelity reward, guided by a multi-site whole-body impedance reference controller, extending classical Cartesian impedance to any controlled link; (2) A bounded residual policy that edits the per-link impedance equilibrium over a frozen base, correcting the coarse but structured wrench estimate supplied by a force encoder co-trained behind a gradient barrier; (3) A Phong-weighted force-origin sampler with an axis-decoupled pelvis anchor induces lower-body-inclusive compliance curriculum training via two interpretable parameters. We evaluate CompliantWBC in simulation against both compliant and stiff baselines, achieving best compliant fidelity of 2.58cm deviation from analytical solutions, and demonstrate it on a real heavy humanoid across static/dynamic force reaction, board wiping, squat under payload, and cooperative payload transport. Project website: https://dotandung.github.io/compliantwbc/

Abstract

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