Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation (2303.11057v3)

Abstract: Understanding and manipulating deformable objects (e.g., ropes and fabrics) is an essential yet challenging task with broad applications. Difficulties come from complex states and dynamics, diverse configurations and high-dimensional action space of deformable objects. Besides, the manipulation tasks usually require multiple steps to accomplish, and greedy policies may easily lead to local optimal states. Existing studies usually tackle this problem using reinforcement learning or imitating expert demonstrations, with limitations in modeling complex states or requiring hand-crafted expert policies. In this paper, we study deformable object manipulation using dense visual affordance, with generalization towards diverse states, and propose a novel kind of foresightful dense affordance, which avoids local optima by estimating states' values for long-term manipulation. We propose a framework for learning this representation, with novel designs such as multi-stage stable learning and efficient self-supervised data collection without experts. Experiments demonstrate the superiority of our proposed foresightful dense affordance. Project page: https://hyperplane-lab.github.io/DeformableAffordance

• The paper presents a novel foresightful dense affordance that estimates long-term outcomes to overcome local optimum traps in multi-step deformable object manipulation.
• It employs a multi-stage stable learning framework with self-supervised data collection, including a Fold to Unfold strategy for enhanced policy optimization.
• Experimental results across various tasks demonstrate higher success rates over baselines and effective simulation-to-real transfer with a Franka Panda robot.

Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation

This paper addresses the complex task of deformable object manipulation, a domain that presents unique challenges due to the intricate states, dynamic properties, and the high-dimensional action spaces associated with objects such as ropes and fabrics. The paper introduces a novel approach using dense visual affordance, specifically targeting the need to avoid local optimum traps in multi-step manipulation tasks.

The authors propose the concept of foresightful dense affordance, which builds on the traditional visual affordance concept by incorporating long-term value estimation for deformable object manipulation tasks. This foresightful affordance helps in estimating the outcome of potential actions over a sequence, overcoming the limitations of greedy policies that may drive the state only towards temporary optimal configurations without achieving the ultimate task objective.

The framework for learning these affordances leverages innovations such as multi-stage stable learning and efficient self-supervised data collection. The authors employ a Dense Visual Affordance approach with picking and placing policies, iteratively optimized across various stages from simple to more complex object configurations. The paper also introduces a Fold to Unfold method for data collection, bridging the challenge of acquiring diverse and multi-stage interaction data vital for training models that generalize well across different object states.

Experimental evaluations on tasks such as cable-ring, cable-ring-notarget, SpreadCloth, and RopeConfiguration demonstrate the proposed method's superiority. The results show higher success rates and normalized scores compared to several baseline models, including reinforcement learning-based methods and Transporter models, highlighting the comprehensive capability of dense affordance in navigating complex manipulation sequences without expert demonstrations.

Furthermore, the paper pushes the boundaries of current methods by successfully translating simulation models to real-world implementation, substantiated through controlled experiments with a Franka Panda robot. This real-world applicability underscores the potential practical utility of foresightful dense affordance in robotic manipulation tasks in domestic or industrial settings.

In terms of future implications, this work suggests critical directions such as further refinement of affordance models for broader classes of deformable objects and enhancing real-time adaptation capabilities for robots operating in dynamically changing environments. It also opens pathways for integrating such visual affordance systems with more sophisticated perception modules to enable robots to not only act but to predictively interact based on foresightful evaluations of their environments. The paper provides strong numerical evidence supporting its claims, setting a robust foundation for follow-up research and practical applications in AI-driven robotic manipulation systems.