Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation (2309.00987v2)

Abstract: Many real-world manipulation tasks consist of a series of subtasks that are significantly different from one another. Such long-horizon, complex tasks highlight the potential of dexterous hands, which possess adaptability and versatility, capable of seamlessly transitioning between different modes of functionality without the need for re-grasping or external tools. However, the challenges arise due to the high-dimensional action space of dexterous hand and complex compositional dynamics of the long-horizon tasks. We present Sequential Dexterity, a general system based on reinforcement learning (RL) that chains multiple dexterous policies for achieving long-horizon task goals. The core of the system is a transition feasibility function that progressively finetunes the sub-policies for enhancing chaining success rate, while also enables autonomous policy-switching for recovery from failures and bypassing redundant stages. Despite being trained only in simulation with a few task objects, our system demonstrates generalization capability to novel object shapes and is able to zero-shot transfer to a real-world robot equipped with a dexterous hand. Code and videos are available at https://sequential-dexterity.github.io

• The paper introduces a bi-directional reinforcement learning framework that chains sub-policies to tackle complex, long-horizon manipulation tasks efficiently.
• It employs a Transition Feasibility Function to optimize switching between dexterous sub-policies, enhancing robustness without re-grasping.
• Experiments validate the method’s ability to generalize and achieve successful zero-shot transfer from simulation to real-world robotic hardware.

Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation

The paper, "Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation," addresses the challenges inherent in deploying dexterous hands for complex, long-horizon manipulation tasks that consist of several distinct and sequenced subtasks. Through a system called Sequential Dexterity, the authors propose a solution leveraging reinforcement learning (RL) to develop a series of dexterous manipulation policies, effectively chaining them to accomplish these long-horizon goals. This work is significant within the domain of robotics, addressing the complex interplay between the high-dimensional action spaces of dexterous hands and the compounding dynamics of extended task sequences.

System Architecture and Methodology

The system's core innovation is a bi-directional optimization framework designed to operationalize the dexterous hand's adaptability and transition capability across varied subtask demands without re-grasping or requiring external tools. This framework encompasses three main components:

1. Training of Dexterous Sub-Policies: Each subtask is defined as a Markov Decision Process (MDP) to construct individual sub-policies using PPO. These sub-policies are first trained independently with a forward process that relies on terminal states from completed subtasks to initialize successor policies.
2. Transition Feasibility and Bi-Directional Optimization: A Transition Feasibility Function is introduced to assess the viability of initiating the subsequent sub-policy from given states, thus ensuring smooth transitions. This function encapsulates temporal sequences of preceding states to provide comprehensive contextual feedback, which is applied in a backward fine-tuning step to refine earlier policies based on the feasibility at the start of future policies.
3. Policy Switching Mechanism: Rather than adhering to rigid pre-defined timing for policy transitions, the system autonomously determines the optimal switching points using real-time evaluations of the transition feasibility. This approach significantly enhances the robustness of the policy chain, improving task success rates and allowing dynamic recovery and bypassing of redundant stages.

Experimental Validation

The framework is empirically validated through two manipulation tasks: constructing structures from Mega Bloks and reorienting tools, each requiring multiple dexterous policy chains. In simulations, the proposed system demonstrated a superior ability to generalize to novel tasks and objects compared to baseline methods, as evidenced by its success rate improvements across both trained and unseen task scenarios.

Furthermore, the paper highlights the real-world applicability of their system, presenting zero-shot transfer results where the robot executed learned policies directly on physical hardware with compelling performance. A noteworthy insight from these experiments emphasizes the advantage of bi-directional training over previous methods, particularly by leveraging the temporal integration of state history in the feasibility model, yielding more effective policy chaining.

Implications and Future Work

The implications of this work extend to various facets of robotics and AI. The introduction of a transition feasibility function presents a novel means of regularizing transitions in hierarchical policies, promising advancements in autonomous manipulation systems. Moreover, this methodology is capable of being generalized beyond dexterous hands to encompass broader robotic applications or collaborative multi-agent systems.

Looking forward, further exploration into improving material handling and insertion tasks could involve better modeling of contact-rich interactions, potentially incorporating physical and tactile feedback to augment sensory information relied upon during manipulation. Additionally, extending the framework to incorporate more autonomous high-level planning could also realize further robust performance across divergent complex manipulation scenarios.

In conclusion, Sequential Dexterity represents a significant contribution within robotic manipulation, providing a robust framework for addressing the inherent challenges of dexterous long-horizon tasks. This system advances the frontier of robot dexterity towards achieving human-level adaptability and intelligence in complex object manipulation.