Coordi-pi: Coordination-Aware Dual-Hand Action Representation for Bimanual Dexterous Manipulation

Yiming Meng; Yanming Shao; Weijie Shen; Kunyang Lin; Jiayu Sun; Ping Luo; Yao Mu

cs.ROOct 14, 2026

Coordi-pi: Coordination-Aware Dual-Hand Action Representation for Bimanual Dexterous Manipulation

Authors: Yiming Meng, Yanming Shao, Weijie Shen, Kunyang Lin, Jiayu Sun, Ping Luo, Yao Mu

Organizations: Shanghai Jiao Tong University · The University of Hong Kong

Abstract

Vision-language-action (VLA) models have shown strong potential for general-purpose robotic manipulation, but extending them to bimanual dexterous manipulation introduces a high-dimensional and structured action space. Existing bimanual VLA policies commonly concatenate right- and left-side arm--hand actions at each control timestep, leaving cross-hand interactions implicit within a single action vector. We introduce Coordi- $π$ , a structured action representation for bimanual dexterous manipulation. Coordi- $π$ represents the right- and left-side arm--hand actions as individual action tokens and organizes them into an interleaved sequence, explicitly exposing the two sides within the action sequence. To explicitly encode hand identity and temporal synchronization in this interleaved representation, a learnable hand-identity embedding distinguishes the two sides, while sequence--control-timestep factorized rotary positional encoding (RoPE) separates sequence position from control timestep, allowing actions at the same control timestep to share a temporal coordinate while retaining distinct sequence positions. Across all five bimanual dexterous manipulation tasks in DexJoCo, Coordi- $π$ achieves state-of-the-art performance under both rand-obj and rand-full, improving the average success rate over $π_{0.5}$ from 31.9% to 51.3% and from 18.3% to 32.3%, respectively. Coordi- $π$ also consistently outperforms $π_{0.5}$ across four real-world bimanual dexterous manipulation tasks.

Figures & tables

\fnum@figure : Overview of Coordi- $\pi$ . Within the action expert, (a) joint bimanual actions are split into interleaved right- and left-side tokens, and (b) learnable hand-identity embeddings distinguish the two sides. (c) Factorized RoPE encodes sequence position and control timestep separately: paired actions occupy different sequence positions but share the same control-timestep coordinate. Right: Conditioned on images, language, and robot state, the action expert generates an action chunk from noise. Output tokens are paired by control timestep into joint bimanual commands, without changing the action horizon.

\fnum@figure : Overview of the evaluation tasks. (a) All five bimanual dexterous manipulation tasks in DexJoCo under rand-obj and rand-full . (b) Four real-world bimanual dexterous manipulation tasks.

Method	Hanoi		Photograph		Unlock iPad		Assembly		Microwave		Avg.
Method	Obj	Full	Obj	Full	Obj	Full	Obj	Full	Obj	Full	Obj	Full
ACT [ 26 ]	$6.0\pm 2.0$	$4.7\pm 2.3$	$7.3\pm 1.2$	$3.3\pm 1.2$	$9.3\pm 3.1$	$0.7\pm 1.2$	$0.0\pm 0.0$	$0.0\pm 0.0$	$66.0\pm 2.0$	$50.0\pm 6.9$	$17.7$	$11.7$
DP-T [ 27 ]	$24.7\pm 4.6$	$0.7\pm 1.2$	$56.7\pm 4.6$	$7.3\pm 1.2$	$8.0\pm 2.0$	$2.0\pm 2.0$	$4.7\pm 3.1$	$0.0\pm 0.0$	$73.3\pm 11.6$	$21.3\pm 4.6$	$33.5$	$6.3$
DP-C [ 27 ]	$12.7\pm 3.1$	$9.3\pm 6.1$	$24.0\pm 8.7$	$8.7\pm 4.2$	$\mathbf{52.0\pm 2.0}$	$12.0\pm 3.5$	$3.3\pm 1.2$	$0.0\pm 0.0$	$54.0\pm 12.5$	$62.7\pm 6.4$	$29.2$	$18.5$
DP3 [ 36 ]	$26.0\pm 2.0$	$13.3\pm 4.2$	$24.0\pm 9.2$	$13.3\pm 9.5$	$16.0\pm 4.0$	$13.3\pm 1.2$	$4.7\pm 1.2$	$0.0\pm 0.0$	$45.3\pm 16.2$	$36.7\pm 6.1$	$23.2$	$15.3$
GR00T N1.5 [ 37 ]	$0.7\pm 1.2$	$0.0\pm 0.0$	$40.7\pm 7.0$	$18.7\pm 7.0$	$12.7\pm 11.0$	$0.0\pm 0.0$	$0.7\pm 1.2$	$1.3\pm 1.2$	$50.7\pm 4.6$	$42.0\pm 7.2$	$21.1$	$12.4$

TABLE I: Success rates (%) on all five bimanual tasks in DexJoCo. Obj and Full denote rand-obj and rand-full , respectively. Entries are mean

\pm

standard deviation; best results per task and setting are bold.

	Inter- leaved	+ Hand ID	+ RoPE	Coordi- $\pi$
Hand ID	✗	✓	✗	✓
Factorized RoPE	✗	✗	✓	✓
Hanoi	$37.3\pm 9.9$	$43.3\pm 7.0$	$39.3\pm 4.6$	$\mathbf{48.7\pm 1.2}$
Photograph	$58.0\pm 5.3$	$69.3\pm 6.4$	$67.3\pm 3.1$	$\mathbf{72.0\pm 6.0}$
Unlock iPad	$29.3\pm 7.0$	$45.3\pm 13.3$	$49.3\pm 5.8$	$\mathbf{52.0\pm 3.5}$
Assembly	$4.7\pm 1.2$	$3.3\pm 2.3$	$2.7\pm 1.2$	$\mathbf{7.3\pm 1.2}$

TABLE II: Ablation success rates (%) under rand-obj on all five bimanual dexterous manipulation tasks in DexJoCo. All variants use the interleaved action representation. Best results are shown in bold.

	$\pi_{0.5}$			DP3			Interleaved			Coordi- $\pi$
Task	RCS	TCS	BCS	RCS	TCS	BCS	RCS	TCS	BCS	RCS	TCS	BCS
Hanoi	91.00	73.77	82.39	89.17	67.49	78.33	96.00	83.50	89.75	93.00	79.65	86.32
Photograph	68.79	68.52	68.65	41.58	30.46	36.02	71.31	70.33	70.82	72.58	76.57	74.58
Unlock iPad	77.91	62.17	70.04	64.05	35.68	49.86	75.10	58.96	67.03	81.19	70.40	75.80
Assembly	80.56	35.31	57.94	76.17	22.30	49.23	72.57	30.16	51.37	81.22	41.57	61.39
Microwave	98.00	67.21	82.61	97.93	58.85	78.39	99.33	75.54	87.44	100.00	82.43	91.21

TABLE III: Bimanual coordination scores on all five bimanual dexterous manipulation tasks in DexJoCo under rand-obj . RCS, TCS, and BCS range from 0 to 100; higher is better.

\fnum@figure : Real-world platform and teleoperation setup: Marvin Pro with two BrainCo Revo 2 hands, a PICO headset, and MANUS data gloves for demonstration collection.

\fnum@figure : Representative real-world rollouts of Coordi- $\pi$ . Each row shows one task, with successive stages ordered from left to right.

\fnum@figure : Real-world success rates of

\pi_{0.5}

and Coordi-

\pi

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Vision-language-action (VLA) models show strong capabilities in single and dual-arm robotic manipulation. Prior works show coordinated bimanual behaviors can emerge from end-to-end learning, leveraging large vision-language backbones with continuous action prediction. However, as bimanual tasks become tightly coupled and execution constraints become critical, implicit coordination alone is insufficient to ensure reliable, interpretable, and stable behavior. In this work, we propose Co-VLA, a coordination-aware bimanual manipulation framework introducing explicit structural priors into VLA models. We instantiate our method on a state-of-the-art vision-language backbone by replacing its monolithic action head with a Structured Action Expert (SAE) designed for bimanual coordination. Specifically, we introduce explicit structure at the action generation level with a modular coordination-aware loss that shapes shared and residual latents according to task-specific structures. The shared latent encodes task-level coordination intent, while residual latents capture execution adjustments for each arm. At deployment, a Latent-Aware Controller (LAC) interprets the learned representations to modulate synchronization strength, execution asymmetry, smoothness, and safety constraints in real time. LAC operates at the joint-command level and remains compatible with standard control pipelines without requiring force or impedance control. Experiments across simulation and real-world benchmarks show Co-VLA significantly outperforms monolithic baselines, achieving a 27% success rate gain in tight-coordination tasks, more than doubling performance in OOD real-world scenarios (from 13% to 27%), and reducing task completion time by up to 25%.

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