GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware Policy (2309.09051v3)

Abstract: Due to the inherent uncertainty in their deformability during motion, previous methods in deformable object manipulation, such as rope and cloth, often required hundreds of real-world demonstrations to train a manipulation policy for each object, which hinders their applications in our ever-changing world. To address this issue, we introduce GenDOM, a framework that allows the manipulation policy to handle different deformable objects with only a single real-world demonstration. To achieve this, we augment the policy by conditioning it on deformable object parameters and training it with a diverse range of simulated deformable objects so that the policy can adjust actions based on different object parameters. At the time of inference, given a new object, GenDOM can estimate the deformable object parameters with only a single real-world demonstration by minimizing the disparity between the grid density of point clouds of real-world demonstrations and simulations in a differentiable physics simulator. Empirical validations on both simulated and real-world object manipulation setups clearly show that our method can manipulate different objects with a single demonstration and significantly outperforms the baseline in both environments (a 62% improvement for in-domain ropes and a 15% improvement for out-of-distribution ropes in simulation, as well as a 26% improvement for ropes and a 50% improvement for cloths in the real world), demonstrating the effectiveness of our approach in one-shot deformable object manipulation.

• The paper introduces a parameter-aware policy that enables one-shot manipulation of deformable objects using a single real-world demonstration.
• It conditions manipulation strategies on simulated physical parameters like Young’s modulus and Poisson’s ratio for dynamic adaptability.
• Empirical results demonstrate a 62% improvement in simulation for in-domain ropes and significant real-world boosts for both ropes and cloths.

Overview of GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware Policy

The paper introduces GenDOM, a paradigm shift in deformable object manipulation by leveraging a parameter-aware policy that allows for the manipulation of deformable objects with just a single real-world demonstration. The research overcomes the traditional need for extensive real-world training samples by training manipulation policies conditioned on parameters characterizing the deformable objects' physical properties, specifically focusing on Young’s modulus and Poisson’s ratio.

Deformable object manipulation (DOM) has posed considerable challenges due to the unpredictability associated with the objects' deformation dynamics. Historically, accurate manipulation required thousands of real-world demonstrations to cater to the variability in elasticity and other deformation characteristics encountered in objects such as ropes and cloths. This requirement has made robust training a costly and laborious endeavor, severely limiting the scalability and adaptability of robotic applications.

To address these challenges, GenDOM utilizes a two-pronged approach: First, it conditions manipulation policies on deformable object parameters. By training these policies with a comprehensive range of simulated parameters, GenDOM can dynamically adjust manipulation strategies based on the specific physical attributes of objects. This parameter-based conditioning allows the manipulation policy to be flexible and generalizable across various object types and tasks.

Second, during inference, GenDOM employs a novel gradient-based optimization technique to estimate the deformable object parameters using a single real-world demonstration. By minimizing the disparity between the point cloud representations from real-world demonstrations and simulated outcomes in a differentiable physics simulator, it accurately determines the parameters that describe an object's deformability. This integration of real-time parameter estimation facilitates the effective deployment of GenDOM in practical scenarios.

Empirical validation of GenDOM confirms its significant performance gains over existing baselines. In simulation, GenDOM demonstrated a 62% improvement for in-domain ropes and a 15% enhancement for out-of-distribution ropes. In real-world settings, it achieved a 26% improvement for ropes and a 50% boost for cloths. These results underscore GenDOM's robust adaptability and superior generalization capacity.

Implications and Future Directions

Practically, GenDOM simplifies and streamlines the process of developing manipulation strategies for a variety of deformable objects without the cumbersome requirement of collecting a vast dataset for each object type. This reduces the obstructions to scalability in developing versatile robotic systems capable of operating in dynamic environments with unforeseen objects and tasks.

Theoretically, the introduction of parameter-aware policies signals a shift in how policies may be tailored to context-specific scenarios in AI, emphasizing the adaptation to underlying physical properties rather than attempting generalized training across immutable models.

Future developments could expand upon this paradigm by exploring additional parameters that further characterize the physical nuances of deformable objects, potentially widening the range of applicable tasks. Moreover, integrating real-time adaptive control mechanisms could prove beneficial to respond dynamically to unexpected mid-task dynamics.

The robustness of parameter-aware policies might also be tested beyond DOM, finding applications in other domains requiring adaptive, context-specific decision-making processes, thereby broadening the impact of this approach in AI and robotics. As simulation and real-world testing environments continue to improve, GenDOM's capabilities could be tested on more complex materials and broader tasks, increasing its utility in industrial and domestic robotics applications.