Learning Coordinated Bimanual Manipulation Policies using State Diffusion and Inverse Dynamics Models (2503.23271v1)

Abstract: When performing tasks like laundry, humans naturally coordinate both hands to manipulate objects and anticipate how their actions will change the state of the clothes. However, achieving such coordination in robotics remains challenging due to the need to model object movement, predict future states, and generate precise bimanual actions. In this work, we address these challenges by infusing the predictive nature of human manipulation strategies into robot imitation learning. Specifically, we disentangle task-related state transitions from agent-specific inverse dynamics modeling to enable effective bimanual coordination. Using a demonstration dataset, we train a diffusion model to predict future states given historical observations, envisioning how the scene evolves. Then, we use an inverse dynamics model to compute robot actions that achieve the predicted states. Our key insight is that modeling object movement can help learning policies for bimanual coordination manipulation tasks. Evaluating our framework across diverse simulation and real-world manipulation setups, including multimodal goal configurations, bimanual manipulation, deformable objects, and multi-object setups, we find that it consistently outperforms state-of-the-art state-to-action mapping policies. Our method demonstrates a remarkable capacity to navigate multimodal goal configurations and action distributions, maintain stability across different control modes, and synthesize a broader range of behaviors than those present in the demonstration dataset.

• The paper proposes a framework combining state diffusion models and inverse dynamics to learn coordinated bimanual manipulation policies for robots.
• A diffusion model predicts future states by iterative refinement, while an inverse dynamics model translates these states into robot actions, enabling robust control in complex scenarios.
• Evaluation shows the approach outperforms state-of-the-art methods, achieving 29.3% success on unseen task combinations in the Franka Kitchen benchmark and enhancing adaptability for bimanual systems.

Learning Coordinated Bimanual Manipulation Policies Using State Diffusion and Inverse Dynamics Models

The paper "Learning Coordinated Bimanual Manipulation Policies Using State Diffusion and Inverse Dynamics Models" explores the integration of state prediction and inverse dynamics modeling to enhance the coordination capabilities of bimanual robotic systems. In robotics, while humans effortlessly coordinate both hands for tasks like laundry or cooking by predicting how their actions will alter the environment, replicating such dexterity in machines remains complex. The researchers address these challenges by infusing predictive human-like manipulation strategies into robots through imitation learning.

Methodology

The core contribution of this paper lies in disentangling the task-related state transitions from agent-specific dynamics to improve bimanual coordination. This is achieved via a two-pronged approach:

1. State Diffusion Model: The authors employ a diffusion model, a variant of the Denoising Diffusion Probabilistic Model (DDPM), to predict future states of the world based on historical data. This model iteratively refines state predictions by removing noise, thereby envisioning the future trajectory of objects given past observations. The diffusion model not only supports handling complex environments involving multimodal goals but is also pivotal in maintaining interaction-aware control across divergent task scenarios.
2. Inverse Dynamics Model: Complementing the diffusion model, an inverse dynamics model translates predicted states into actions that the robot must take to transition an object from its current to a desired state. This separation allows the system to maintain consistent, effective manipulation in tasks involving deformable and multiple objects, utilizing a feedback mechanism to improve policy generation by incorporating both historical and future states.

Evaluation and Results

The framework is tested across diverse scenarios including simulation benchmarks and real-world tasks. The paper introduces environments such as Block Pushing, Franka Kitchen, and Push-L tasks, each exhibiting different challenges in terms of multimodal goal configurations and dynamic object interactions. Experimental results indicate that the proposed model outperforms state-of-the-art state-to-action mapping policies, particularly exploiting the advantages of explicit state modeling to enhance stability and broaden behavior synthesis beyond observed demonstrations. For instance, in the Franka Kitchen task, the model demonstrated a notable 29.3% success rate in achieving five tasks with position control, a feat not originally demonstrated in the training dataset.

Implications and Future Research

The implications of this work are bidirectional. Practically, integrating prediction models with inverse dynamics could revolutionize the autonomy and adaptability of bimanual robotic systems in unstructured environments. Theoretically, it opens pathways for future research on increasing sample efficiency and training large-scale robotic systems with more diverse, real-world human demonstrations. Moreover, it indirectly challenges the confines of existing imitation learning frameworks, suggesting potential expansions into realms that demand high flexibility and complex multi-agent coordination.

As a future direction, optimizing the training phase to reduce extensive data requirements while enhancing model generalization remains a fertile area of exploration. The fusion of robust perception systems with dynamic model learning further underscores how AI could seamlessly integrate into human-centric tasks, enhancing collaborative capabilities between robots and humans.