MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale (2104.08212v2)

Abstract: General-purpose robotic systems must master a large repertoire of diverse skills to be useful in a range of daily tasks. While reinforcement learning provides a powerful framework for acquiring individual behaviors, the time needed to acquire each skill makes the prospect of a generalist robot trained with RL daunting. In this paper, we study how a large-scale collective robotic learning system can acquire a repertoire of behaviors simultaneously, sharing exploration, experience, and representations across tasks. In this framework new tasks can be continuously instantiated from previously learned tasks improving overall performance and capabilities of the system. To instantiate this system, we develop a scalable and intuitive framework for specifying new tasks through user-provided examples of desired outcomes, devise a multi-robot collective learning system for data collection that simultaneously collects experience for multiple tasks, and develop a scalable and generalizable multi-task deep reinforcement learning method, which we call MT-Opt. We demonstrate how MT-Opt can learn a wide range of skills, including semantic picking (i.e., picking an object from a particular category), placing into various fixtures (e.g., placing a food item onto a plate), covering, aligning, and rearranging. We train and evaluate our system on a set of 12 real-world tasks with data collected from 7 robots, and demonstrate the performance of our system both in terms of its ability to generalize to structurally similar new tasks, and acquire distinct new tasks more quickly by leveraging past experience. We recommend viewing the videos at https://karolhausman.github.io/mt-opt/

Essay on "MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale"

The paper "MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale" investigates the potential of a large-scale, collective robotic learning system to enable effective multi-task learning and transferability in robotics. The paper introduces MT-Opt, a framework that scales multi-task reinforcement learning (RL) in robotic systems by effectively sharing exploration, experiences, and learned representations across different tasks.

A key focus of this research lies in addressing the challenges associated with training general-purpose robotic systems through RL, given the extensive time requirements typically needed for acquiring each discrete skill. The authors propose a systematic approach where both novel and structurally similar tasks can leverage shared experiences from previously learned endeavors, thereby reducing the overhead of learning in isolation.

Framework and Methodology

Central to the MT-Opt approach are three main components:

1. Scalable Task Specification: The system enables intuitive task specification via user-provided examples of desired outcomes. This results in the efficient definition of task rewards through a success-detector model, which is crucial for handling the complexity inherent in multi-task environments.
2. Multi-Robot Collective Learning: Experiential data is concurrently collected from multiple tasks via a collaborative, multi-robot setup. This serves to bootstrap simpler tasks while facilitating exploration for more complex tasks. The paper illustrates tasks like semantic picking and object placement benefitting from collaborative data accruement.
3. Multi-Task RL Algorithm: MT-Opt introduces a multi-task RL algorithm designed to share both parameters and data representations among tasks. It crucially features task impersonation, where episodes collected for one task are leveraged beneficially for others, and strategic data rebalancing to mitigate task-data imbalance issues.

Experimental Results

Quantitative evaluations validate that MT-Opt markedly outperforms baseline methods across a diverse set of robot manipulation tasks. For example, the system achieves an impressive 89% success rate on a generalized task like lift-any, with notable gains in more specialized tasks such as lift-carrot and semantic placing tasks. The tasks range from generic object lifting to intricate object-manipulation scenarios, indicating the robustness of the shared-learning framework.

Moreover, the authors meticulously demonstrate how MT-Opt enables more rapid achievement of new tasks through experience transfer, showcasing significant advantages over single-task learning frameworks. In certain instances, MT-Opt demonstrated a tenfold improvement in learning efficiency for new tasks.

Implications and Future Work

The implications of this research are substantial, offering pathways towards more efficient and scalable robotic learning protocols. The paper effectively challenges traditional single-task paradigms, underscoring the value of shared experiences and representations in accelerating task mastery.

The work also paves the way for future research that could further explore task-skill groupings, leveraging automated determination of task relationships for more dynamic and data-efficient transfer learning. Another potential line of inquiry could advance the exploration of hierarchical reinforcement techniques to decompose complex tasks into simpler interdependent subtasks, thereby enhancing performance through structured task representations.

Overall, MT-Opt makes compelling strides toward the realization of more versatile and general-purpose robot learning systems, showcasing how multi-task reinforcement learning can be adeptly positioned to scale across multiple skill domains, ultimately contributing to more capable and adaptive autonomous systems.