ContactExplorer: Contact Coverage-Guided Exploration for General-Purpose Dexterous Manipulation

Zixuan Liu; Ruoyi Qiao; Chenrui Tie; Xuanwei Liu; Yunfan Lou; Chongkai Gao; Zhixuan Xu; Lin Shao

cs.ROMar 11, 2026

ContactExplorer: Contact Coverage-Guided Exploration for General-Purpose Dexterous Manipulation

Authors: Zixuan Liu, Ruoyi Qiao, Chenrui Tie, Xuanwei Liu, Yunfan Lou, Chongkai Gao, Zhixuan Xu, Lin Shao

Organizations: School of Computing, National University of Singapore · RoboScience

Abstract

Reinforcement learning explores effectively in domains such as Atari games, navigation, and locomotion, where novelty over states or dynamics is a sufficient signal. In contrast, dexterous manipulation requires rich physical hand--object interactions, but existing methods often suffer from unstable contact-based novelty signals, inefficient distance novelty signals, or reliance on task-specific priors. We propose ContactExplorer, a general exploration method for dexterous manipulation tasks. ContactExplorer represents contact as the intersection between object surface points and hand keypoints, encouraging dexterous hands to discover diverse and novel contact patterns, namely which fingers contact which object regions. It maintains a contact counter conditioned on discretized object states obtained via learned hash codes. This counter is leveraged in two complementary ways: (1) a count-based contact coverage reward that promotes exploration of novel contact patterns, and (2) an energy-based reaching reward that guides the agent toward under-explored contact regions. We evaluate ContactExplorer on seven contact-rich manipulation tasks and five dexterous hand embodiments. Experimental results show that ContactExplorer substantially improves sample efficiency and success rates over existing exploration methods, that it reduces the need for task-specific priors, and that it remains effective across hand embodiments and transfers to the real world. Project page is https://contact-explorer.github.io.

Figures & tables

Fig. 1: Contact Exploration Strategies. (A) Existing strategies discover contact inefficiently, perturbing only nearby contacts, exploring without requiring contact, or relying on forces that are unreliable under discontinuous contacts. (B) ContactExplorer tracks contact coverage over object surface regions and guides the fingers toward under-covered contacts, yielding effective interaction and faster convergence.

Fig. 2: Hand Keypoint Representation. Sparse surface keypoints on each finger.

Fig. 3: Overview of ContactExplorer. Our contact coverage-guided exploration framework consists of three main components: (1) a learned state hashing module that clusters continuous object states (Sec. III-C ); (2) a contact coverage counter that tracks state-conditioned finger-region interactions (Sec. III-D ); and (3) a structured exploration reward mechanism, decomposed into contact coverage (Sec. IV-A ) and energy-based reaching terms (Sec. IV-B ). In the visualization, the current and goal object states are depicted as distinctly colored point clouds.

Fig. 4: Learning curves on seven dexterous manipulation tasks. Each panel plots success rate against environment steps for ContactExplorer and four baselines ( TR , LHCC , HaC , RND-Dist ), averaged over five random seeds; shaded regions denote $\pm 1$ standard deviation across seeds. ContactExplorer attains higher final success rates and faster convergence, and is the only method to make progress on the hard-exploration Constrained Object Retrieval task, where all baselines fail.

Fig. 5: Task overview. Seven contact-rich tasks, each demanding a distinct interaction pattern. (a) Cluttered Object Singulation : extract a target from a densely packed row. (b) Constrained Object Retrieval : retrieve an object from a walled space. (c) In-Hand Reorientation : rotate an object to a goal orientation. (d) Bimanual Object Opening : two hands open an articulated object. (e) Bimanual Board Lifting : two hands lift an oversized board. (f) Grasping : grasp and lift a target. (g) Tiled Object Retrieval : retrieve a cube from a tightly tiled bin.

Budget	Method	Task							Avg.
Budget	Method	Singulation	Constrained Retrieval	In-Hand Reorientation	Bimanual Opening	Board Lifting	Grasping	Tiled Retrieval	Avg.
40%	TR	$72{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 34}}$	$0{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 0}}$	$64{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 16}}$	$74{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 25}}$	$27{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 37}}$	$52{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 25}}$	$39{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 7}}$	$47$
	LHCC [ 12 , 43 ]	$34{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 26}}$	$0{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 0}}$	$64{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 14}}$	$67{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 25}}$	$18{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 37}}$	$44{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 25}}$	$42{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 14}}$	$38$
	HaC [ 14 ]	$14{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 19}}$	$0{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 0}}$	$64{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 13}}$	$51{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 38}}$	$38{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 47}}$	$29{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 23}}$	$41{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 6}}$	$34$
	RND-Dist [ 13 ]	$67{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 33}}$	$0{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 0}}$	$66{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 13}}$	$69{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 26}}$	$19{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 39}}$	$27{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 31}}$	$43{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 7}}$	$42$
	Ours	$\mathbf{94}{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 3}}$	$\mathbf{67}{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 34}}$	$\mathbf{76}{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 14}}$	$\mathbf{85}{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 9}}$	$\mathbf{75}{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 16}}$	$\mathbf{72}{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 8}}$	$\mathbf{57}{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 11}}$	$\mathbf{75}$

TABLE I: Main results across all tasks under full and 40% training budgets. Per-task SR in %, mean

\pm

std over five seeds; best in bold. Avg. is the unweighted mean of the seven per-task means.

Fig. 6: Task-Specific Initialization Prior. Default and contact-aware initialization in Constrained Object Retrieval .

Method	40% budget	Full budget
TR	$0{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 0}}$	$0{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 0}}$
TR-ContactInit	$25{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 32}}$	$33{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 40}}$
Ours	$\mathbf{67}{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 34}}$	$\mathbf{88}{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 6}}$

TABLE II: Comparison with Task-Specific Prior. SR in %, mean

\pm

std over five seeds; best in bold.

Reward terms	40% budget		Full budget
Reward terms	Sing.	Constrained Retri.	Sing.	Constrained Retri.
Energy only	$88{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 4}}$	$12{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 15}}$	$94{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 2}}$	$36{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 44}}$
Contact only	$73{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 23}}$	$31{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 38}}$	$89{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 7}}$	$36{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 44}}$
Ours	$\mathbf{94}{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 3}}$	$\mathbf{67}{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 34}}$	$\mathbf{96}{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 1}}$	$\mathbf{88}{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 6}}$

TABLE III: Reward Ablation Studies. Sing. and Constrained Retri. denote Cluttered Object Singulation and Constrained Object Retrieval, respectively. SR in %, mean

\pm

std over five seeds; best in bold.

Fig. 7: Box Push diagnostic. Learned state clusters keep the contact statistics of the two initial configurations separate.

Fig. 9: Open-Loop Trajectory Replay. Replaying privileged teacher actions open-loop on the real robot produces consistent 90 ° $z$ -axis rotations.

Fig. 10: Real-World Policy Execution. A representative rollout (left to right) of the learned policy performing Cluttered Object Singulation .

Method	Exposure	Transport
TR	$11/30~(36.7\%)$	$1/30~(3.3\%)$
Ours	$\mathbf{23/30~(76.7\%)}$	$\mathbf{10/30~(33.3\%)}$

TABLE IV: Real-World Cluttered Object Singulation Results. Successes over 30 trials; Transport additionally requires moving the exposed object to the goal.

Parameter	Value
Parallel environments	2,048
Learning-rate schedule	KL-adaptive
PPO epochs per update	5
Minibatches per epoch	4
Discount factor $\gamma$	0.99
GAE parameter $\lambda_{\mathrm{GAE}}$	0.95

TABLE V: PPO Hyperparameters.

Parameter	Isaac Gym		mjlab
Parameter	Manipulation	Reorientation	Reorientation	Grasping
Contact coefficient $\alpha$	200	240	128	128
Reaching coefficient $\beta$	1.28	2.56	0.5	0.5
Hash length $H$	5	8	5	5
Surface points $M$	512 *	512	512	512
Surface regions $K$	32	32	64	64

TABLE VI: ContactExplorer Parameters. Manipulation includes single-arm and bimanual tasks. AE denotes the object-state autoencoder. ∗ Board lifting uses 1,024 surface points; object opening uses up to 1,024 per articulated part.

$\alpha$	$\beta$	Success rate (%)
		40% budget	Full budget
$50$	$0.32$	$14{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 28}}$	$17{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 35}}$
$100$	$0.64$	$30{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 37}}$	$52{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 42}}$
$200$	$1.28$	$67{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 34}}$	$88{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 6}}$
$400$	$2.56$	$\mathbf{69}{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 16}}$	$\mathbf{89}{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 4}}$
$800$	$5.12$	$40{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 35}}$	$56{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 45}}$

TABLE VII: Exploration Reward Scales. Constrained Object Retrieval SR in %, mean

\pm

std over five seeds. Best mean per column in bold; the default is shaded.

$H$	$\lambda$	Success rate (%)
		40% budget	Full budget
	$0.5$	$68{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 25}}$	$90{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 2}}$
	$1.0$	$63{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 32}}$	$85{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 6}}$
$4$	$2.0$	$49{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 40}}$	$52{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 43}}$
	$0.5$	$64{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 33}}$	$73{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 36}}$
	$1.0$	$67{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 34}}$	$88{\scriptstyle\,{\color[rgb]{0.55,0.55,0.55}\pm 6}}$

TABLE VIII: Object-State Clustering Parameters. Constrained Object Retrieval SR in %, mean

\pm

std over five seeds. Best mean per column in bold; the default is shaded.

Fig. 12: Simulation and Real Point Clouds. Simulated points are blue and real observations are red.

Observation	Dimension
$\mathbf{P}_{t-1:t}^{\mathrm{mask}}$	$2\times 1024\times 4$
$\mathbf{q}_{t-1:t}^{\mathrm{arm}}$	$2\times 7$
$\mathbf{q}_{t-1:t}^{\mathrm{hand}}$	$2\times 16$

TABLE IX: Student Observations. Two-step histories of the scene point cloud with a binary target-mask channel and robot joint positions.

Fig. 13: Real-World Failure Cases. Manipulating a non-target object (left) and failing to stabilize the target after singulation (right).

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Open paper AskPDF arXiv

Glove-based motion capture is emerging as a scalable approach to collecting dexterous-hand demonstration data. However, due to the kinematic gap between the human and robot hand, the recorded human motions cannot be executed directly on the robot, especially for contact-rich tool-use tasks involving in-hand reorientation. Prior work bridges this gap in simulation through reinforcement learning (RL) or trajectory optimization, but the human contact pattern is hard to preserve under such formulations, often producing unnatural manipulation and unstable functional grasps. These methods also train a separate policy or solve a separate optimization for each reference trajectory, which is inefficient. To solve these problems, we propose DexTaG, a tactile-guided RL framework for dexterous manipulation. During training, tactile signals captured by the glove guide policy search toward the measured human contact pattern, reducing reliance on precise reference geometry for contact supervision. To improve efficiency, we train a single generalizable retargeter jointly on all training trajectories of the same object. The retargeter is further distilled into a tactile-free student controller conditioned on the target object trajectory for real-world deployment. On marker-pen and hammer manipulation tasks, DexTaG learns natural, contact-rich behaviors that baselines with distance-based contact heuristics fail to learn, generalizes to held-out trajectories of the same object and task, and outperforms single-trajectory baselines on OakInk2.

Abstract

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