Dual-Arm Coordination Framework

• Dual-arm coordination frameworks are systematic approaches that enable two robotic manipulators to work cooperatively by synchronizing spatial, temporal, and force/motion actions.
• They integrate modular architectures with pipelines for perception, task planning, motion planning, control, and learning-based coordination to optimize performance.
• These frameworks leverage DAG-based task decomposition, dynamic impedance modulation, and digital twin benchmarks to achieve efficient and safe bimanual manipulation under varied conditions.

A dual-arm coordination framework refers to a systematic approach for enabling two robotic manipulators to cooperatively execute tasks that require spatial, temporal, or force/motion synchronization between the arms. These frameworks address a range of challenges, including task decomposition, motion planning, dynamic allocation of sub-tasks, coordinated control, and safe interaction with both the manipulated objects and the environment. State-of-the-art dual-arm coordination frameworks integrate advanced perception, planning, machine learning, and control techniques, facilitating robust performance even in unstructured or dynamic scenarios.

1. Architectural Paradigms and System Components

Dual-arm coordination architectures are usually modular, with pipeline stages for perception, task planning, motion planning, and control. System components often include:

• Perception: Visual and geometric understanding, with pipelines ranging from classic camera/marker setups for pose estimation and feature detection (Zhang et al., 25 Oct 2024), to modern deep-learning backbones (e.g., ResNet50, GraspNet) for dense geometric feature extraction in clutter or with deformable objects (Wang et al., 5 Dec 2024, Wang et al., 4 Apr 2025).
• Task and Motion Planning: Hierarchical or integrated architectures decompose tasks into sub-tasks or action chunks, employing combinatorial search, DAG representations, or learning-based primitives to allocate and schedule actions between the arms (Gao et al., 10 Apr 2024, Gao et al., 14 Jun 2024).
• Action Generation and Execution: Policies are implemented as shared deep networks (e.g., PPO actor-critic, Transformers), expert-script code from LLM-based pipelines, or hybrid base-residual controllers for adaptive motion (Motoda et al., 18 Mar 2025, Tung et al., 2020, Guo et al., 11 May 2025).
• Control and Synchronization: Coordinated action can be achieved with synchronization terms in the loss, joint visual servoing laws, dynamically modulated impedance control, or explicit message-passing between arm controllers (Kumar et al., 22 Nov 2025, Wen et al., 2022).
• Human/Shared Autonomy: Frameworks for telemanipulation integrate user input, impedance reference tracking, and high-level mode switching between independent and coordinated control (Ozdamar et al., 2022).

Comprehensive systems typically unify these components via ROS-based distributed architectures, high-frequency control loops, and real-time perception.

2. Task Planning, Scheduling, and Assignment

A central challenge in dual-arm frameworks is the temporal and spatial allocation of manipulation subtasks to arms:

• DAG-based Decomposition: DAG-Plan formalizes the task as a directed acyclic graph over sub-tasks, labeling each node with arm-action requirements. An LLM generates the DAG based on high-level task instructions and environment state, enabling dynamic scheduling and parallelism. Nodes are assigned to arms at runtime based on cost heuristics combining reachability and distance, and filtered by spatial and collision constraints. The assignment function $f:V\to A$ is typically optimized to minimize the overall makespan (maximum total execution time across both arms) (Gao et al., 14 Jun 2024).
• Skeleton Sampling and Heuristic Search: MODAP and task planners leverage arrangement-space A* and combinatorial backbones to construct action skeletons. These are subsequently refined with motion-level sampling, collision checks, and kinematic reachability constraints. Fixed handoff poses and lazy buffer allocation strategies are often employed to simplify inter-arm synchronization and buffer placement (Gao et al., 10 Apr 2024, Gao et al., 2022).
• Language-Conditioned Task Parsing and Code Generation: Recent frameworks use generative LLM pipelines to decompose tasks into subtasks, infer spatial relations, and yield Python code invoking policy APIs. These pipelines facilitate scalable, annotation-aware scripted task decomposition without extensive manual modeling (Mu et al., 17 Apr 2025, Mu et al., 4 Sep 2024).

Such scheduling approaches support both "occupy–release" action semantics for object handover, and dynamic prioritization of tool use, grasp, and motion primitives tailored to each scenario.

3. Learning-Based Coordination and Policy Architectures

A variety of learning-based architectures have been developed for dual-arm manipulation:

• Shared CNN Actor–Critic (DRL): Frameworks like those in (Wang et al., 5 Dec 2024, Wang et al., 4 Apr 2025) employ convolutional policies that interpret shared or object-centric feature maps and generate dual-arm action primitives (e.g., locations for grasp or push). CNN actor–critic policies may share the trunk (feature encoder) or operate in a strict two-arm factored manner for independent and cooperative primitives.
• Diffusion Policies and Energy-Based Models: UniDiffGrasp introduces a score-based diffusion model (CGDF) for 6-DoF grasp synthesis, augmented with semantic region splitting and energy-based collision/force-closure checks. Independent diffusion runs per arm are paired via candidate filtering for stability and spatial separation (Guo et al., 11 May 2025).
• Transformer-Based Coordination: Action chunking and Inter-Arm Coordinated Transformer Encoders (IACE) facilitate temporal synchronization between arms, allowing policies to output short trajectories ("chunks"), and achieve higher task-thread synchronization and robustness compared to single-stream transformer baselines (Motoda et al., 18 Mar 2025).
• Multi-Task RL with Shared Action Blocks: In humanoid robots, multi-task actor-critic methods (e.g., DiGrad) are adapted to coordinate per-arm controllers with shared-body actions (such as torso), via reward-based penalties for collision and asynchrony, and additional synchronous bonuses for simultaneous grasp success (Phaniteja et al., 2018).

Learning frameworks are typically trained end-to-end via reinforcement learning or behavior cloning from multi-arm human demonstrations, with domain randomization and/or expert-scripted task generation facilitating sim-to-real transfer (Tung et al., 2020, Mu et al., 17 Apr 2025).

4. Motion Planning, Kinematics, and Control Strategies

Coordinated dual-arm execution demands sophisticated motion planning and adaptive, compliant control:

• Integrated Task and Motion Optimization: MODAP and similar pipelines tightly integrate combinatorial task scheduling and trajectory (joint-space) optimization, solving for $(σ, q_1(t), q_2(t))$ to minimize makespan under kinematic, collision, and dynamic constraints (velocity, acceleration, jerk) (Gao et al., 10 Apr 2024).
• Admittance and Impedance Control: Collaborative manipulation frameworks modulate contact force via admittance and impedance controllers, rendering physical interaction safe, compliant, and robust to model uncertainty. Fractal impedance controllers (FIC) provide provable passivity and stability even with multi-operator input (Wen et al., 2022).
• Dynamic Impedance Modulation and Engagement Detection: For precision assembly (e.g., snap-fit), frameworks integrate high-frequency proprioceptive event detection (SnapNet) to trigger mode-switching in impedance parameters, rapidly reducing insertion-axis stiffness and force at critical task moments (Kumar et al., 22 Nov 2025).
• Visual Servoing and Feedback: Image-based visual servoing (IBVS) schemes rely on marker detection and mutual observation between arms' eye-in-hand cameras, reducing pose synchronization errors and fluctuations in interaction wrenches (Zhang et al., 25 Oct 2024). Recent methods couple global and local deep matching to achieve sub-centimeter bottleneck alignment from a single demonstration (2503.06831).

Collision checking, reachability envelopes, and trajectory smoothing (e.g., via splines or temporal ensembling of action chunks) are routine elements in ensuring safe, efficient motion.

5. Open-World Perception, Language, and Benchmarking

Next-generation frameworks exploit generative perception, open-vocabulary language, and synthetic data to address generalization and large-scale evaluation:

• Digital Twin Benchmarks: RoboTwin constructs digital twins from minimal RGB input via 3D generative models, adds keypoint/axis annotations, and employs LLMs for spatial constraint–aware code synthesis. This supports benchmark-standardized evaluation and data-efficient policy pre-training (Mu et al., 17 Apr 2025, Mu et al., 4 Sep 2024).
• Open-Vocabulary Grasping and Task Reasoning: UniDiffGrasp and VLM-SFD utilize modern vision-LLMs for interpreting user instructions, grounding semantic targets, and constraining grasp/action diffusion to specific object parts, enabling flexible dual-arm manipulation that generalizes beyond fixed part categories (Guo et al., 11 May 2025, Chen et al., 16 Jun 2025).

Empirical results show pre-training on diverse synthetic data and LLM-driven pipeline code yields substantial success-rate improvements in real-world, dual-arm settings.

6. Experimental Validation and Benchmark Results

Empirical evaluation across frameworks demonstrates substantial gains in coordination efficiency, task success rates, and policy generalization:

Framework	Core Metric	Dual-Arm Success	Baseline (Single/Naive)	Improvement
MODAP (Gao et al., 10 Apr 2024)	Real-world makespan	≤40% faster	Varied	+13–44%
DAG-Plan (Gao et al., 14 Jun 2024)	Stage efficiency	152.9%	100%	+53%
UniDiffGrasp (Guo et al., 11 May 2025)	Grasp Success Rate	0.767	0.475	+61% (dual-arm GSR)
RoboTwin (Mu et al., 17 Apr 2025)	Dual-Arm Success	~62%	20%	+42% (w/ sim pretrain)
ODIL (2503.06831)	Dual-task accuracy	77.2% avg	RoboTAP <25%	+52 pp
SnapNet+DS (Kumar et al., 22 Nov 2025)	Recall/Impact Force	96.7% / -30%	60–73% / higher force	+20–30 pp, safer

All frameworks report performance on diverse real and/or simulated benchmarks, including kitchen and assembly tasks, dense clutter grasping, teleoperation, deformable object manipulation, and large-scale synthetic evaluation.

7. Trends, Insights, and Limitations

Research indicates that effective dual-arm coordination hinges on:

• Explicit task-level decomposition and assignment (DAGs, LLM code, symbolic plans) to avoid temporal bottlenecks and enable parallelism.
• Shared or fused perception-action abstraction, using feature-level or latent-space fusion to encode object-centric, relation-aware policies.
• Synchronization via controller-level coupling, transformer-based temporal alignment, or explicit event detection for phase transitions or critical contacts.
• Benchmarks and data-generation pipelines (RoboTwin, Multi-Arm RoboTurk) providing scalable, diverse, and high-fidelity demonstration datasets to support robust generalization.

Noted limitations include the need for further advances in collision-aware scheduling, contact-rich skill learning, sim-to-real transfer for deformables and human-in-the-loop collaboration, and scalable annotation of functional part relations.

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Dual-arm coordination frameworks thus represent a multidisciplinary convergence of combinatorial planning, deep perceptual abstraction, advanced control, and large-scale data methodologies, underpinning the current state-of-the-art in robust, versatile, and generalizable robotic bimanual manipulation (Gao et al., 10 Apr 2024, Kumar et al., 22 Nov 2025, Mu et al., 17 Apr 2025, Guo et al., 11 May 2025, Wang et al., 5 Dec 2024, Motoda et al., 18 Mar 2025).