AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer (2302.04903v2)

Abstract: Simulation parameter settings such as contact models and object geometry approximations are critical to training robust robotic policies capable of transferring from simulation to real-world deployment. Previous approaches typically handcraft distributions over such parameters (domain randomization), or identify parameters that best match the dynamics of the real environment (system identification). However, there is often an irreducible gap between simulation and reality: attempting to match the dynamics between simulation and reality across all states and tasks may be infeasible and may not lead to policies that perform well in reality for a specific task. Addressing this issue, we propose AdaptSim, a new task-driven adaptation framework for sim-to-real transfer that aims to optimize task performance in target (real) environments -- instead of matching dynamics between simulation and reality. First, we meta-learn an adaptation policy in simulation using reinforcement learning for adjusting the simulation parameter distribution based on the current policy's performance in a target environment. We then perform iterative real-world adaptation by inferring new simulation parameter distributions for policy training, using a small amount of real data. We perform experiments in three robotic tasks: (1) swing-up of linearized double pendulum, (2) dynamic table-top pushing of a bottle, and (3) dynamic scooping of food pieces with a spatula. Our extensive simulation and hardware experiments demonstrate AdaptSim achieving 1-3x asymptotic performance and $\sim$2x real data efficiency when adapting to different environments, compared to methods based on Sys-ID and directly training the task policy in target environments. Website: https://irom-lab.github.io/AdaptSim/

An Overview of "AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer"

The paper "AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer" by Ren, Dai, Burchfiel, and Majumdar addresses the pivotal challenge in robotics of effectively transferring manipulation policies learned in simulated environments to real-world applications. The inherent difficulties in accurately modeling real-world dynamics in simulations often lead to a gap that can impede policy performance when deployed in actual settings. The authors propose AdaptSim, a novel framework that takes a task-driven approach to simulate adaptations, targeting the enhancement of task performance in real environments.

Core Methodology

AdaptSim operates in two distinct phases. The first phase is focused on meta-learning an adaptation policy within the confines of a simulated environment. This is achieved through reinforcement learning, where the adaptation policy is trained to iteratively update simulation parameter distributions, enhancing the task policy's performance in a variety of simulated target environments. The crux here is the optimization of task-relevant parameters, rather than a myopic focus on aligning simulation to real-world dynamics.

The second phase involves deploying the adaptation policy in the real world. Here, AdaptSim adapts the simulated parameters iteratively, using real-world interactions to refine the policy further. The authors emphasize that the task-driven nature of the adaptation ensures that only the parameters that are directly influential to the task's success are adjusted, optimizing data use efficiency and task performance simultaneously.

Empirical Evaluation

The experiments are comprehensive, covering three robotic tasks that include a classic control task and two manipulation tasks with contact-rich dynamics: double pendulum swing-up, dynamic tabletop pushing, and dynamic scooping. AdaptSim demonstrates superior performance compared to traditional Sys-ID methods and domain randomization strategies across these tasks. Notably, AdaptSim requires fewer iterations over real trajectories to reach an efficacious policy, highlighting its real data efficiency.

Numerical Outcomes and Claims

AdaptSim yields substantial performance metrics, with the authors reporting 1-3x improvements in asymptotic task performance and roughly 2x efficiency in real data usage compared to baselines relying on Sys-ID or naive domain randomization. This is significant, as traditional methods often necessitate extensive modification of simulation parameters to closely mimic real-world counterparts without necessarily improving task-specific performance.

Theoretical and Practical Implications

From a theoretical standpoint, AdaptSim introduces a paradigm shift in sim-to-real transfer approaches by linking the adaptation process more closely to task performance than to simulation fidelity. This could prompt future developments in AI and robotics to explore further task-centric methodologies for efficient policy transfer. Practically, the framework could reduce physical experimentation requirements significantly, enabling more accessible and cost-effective deployment of robotic systems in dynamic environments.

Future Directions

While AdaptSim marks a significant advancement, the paper acknowledges potential limitations such as the assumption of a minimal sim-to-real gap for effective adaptation. This opens avenues for future work to explore environments where the real-world dynamics are vastly different from any simulated conditions encountered during training. Moreover, extending the framework to handle multi-task scenarios could provide additional robustness and versatility in real-world applications.

In conclusion, AdaptSim provides a compelling contribution to the field of robotic control transfer, where understanding and addressing the sim-to-real gap remains a critical challenge. Its task-driven focus aligns learning priorities with real-world objectives, presenting a promising evolution in the pursuit of effective and efficient robotic deployment solutions.