TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction: Related Work

Table of Links

Abstract and 1 Introduction

2 Preliminaries

3 TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction and 3.1 Learning Base Policies in Simulation with RL

3.2 Learning Residual Policies from Online Correction

3.3 An Integrated Deployment Framework and 3.4 Implementation Details

4 Experiments

4.1 Experiment Settings

4.2 Quantitative Comparison on Four Assembly Tasks

4.3 Effectiveness in Addressing Different Sim-to-Real Gaps (Q4)

4.4 Scalability with Human Effort (Q5) and 4.5 Intriguing Properties and Emergent Behaviors (Q6)

5 Related Work

6 Conclusion and Limitations, Acknowledgments, and References

A. Simulation Training Details

B. Real-World Learning Details

C. Experiment Settings and Evaluation Details

D. Additional Experiment Results

5 Related Work

Robot Learning via Sim-to-Real Transfer Physics-based simulations [7–11, 50, 93–95] have become a driving force [1, 2] for developing robotic skills in tabletop manipulation [96–99], mobile manipulation [100–103], fluid and deformable object manipulation [104–107], dexterous in-hand manipulation [14–18], locomotion with various robot morphology [19–27, 108], object tossing [83], acrobatic flight [29, 30], etc. However, the domain gap between the simulators and the reality is not negligible [11]. Successful sim-to-real transfer includes locomotion [19–28], in-hand re-orientation for dexterous hands where objects are initially placed near the robot [14–18], and non-prehensile manipulation limited to simple tasks [31–40]. In this work, we tackle more challenging sim-to-real transfer for complex manipulation tasks and successfully demonstrate that our approach can solve sophisticated contact-rich manipulation tasks. More importantly, it requires significantly fewer real

robot data compared to the prevalent imitation learning and offline RL approaches [68, 69, 72]. This makes solutions that are based on simulators and sim-to-real transfer more appealing to roboticists.

Sim-to-Real Gaps in Manipulation Tasks Despite the complex manipulation skills recently learned with RL in simulation [109], directly deploying learned control policies to physical robots often fails. The sim-to-real gaps [11, 41, 45, 110] that contribute to this performance discrepancy can be coarsely categorized as follows: a) perception gap [19, 42–44], where synthetic sensory observations differ from those measured in the real world; b) embodiment mismatch [19, 45, 46], where the robot models used in simulation do not match the real-world hardware precisely; c) controller inaccuracy [47–49], meaning that the results of deploying the same high-level commands (such as in configuration space [111] and task space [112]) differ in simulation and real hardware; and d) poor physical realism [50], where physical interactions such as contact and collision are poorly simulated [113].

Although these gaps may not be fully bridged, traditional methods to address them include system identification [19, 31, 51, 52], domain randomization [14, 53–55], real-world adaptation [56], and simulator augmentation [58–60]. However, system identification is mostly engineered on a case-bycase basis. Domain randomization suffers from the inability to identify and randomize all physical parameters. Methods with real-world adaptation, usually through meta-learning [114], incur potential safety concerns during the adaptation phase. Most of these approaches also rely on explicit and domain-specific knowledge about tasks and the simulator a priori. For instance, to perform system identification for closing the embodiment gap for a quadruped, Tan et al. [19] disassembles the physical robot and carefully calibrates parameters including size, mass, and inertia. Kim et al. [33] reports that collaborative robots, such as the commonly used Franka Emika robot, have intricate joint friction that is hard to identify and randomized in typical physics simulators. To make a simulator more akin to the real world, Chebotar et al. [40] deploys trained virtual robots multiple times to refine the distributions of simulation parameters. This procedure not only introduces a significant real-world sampling effort, but also incurs potential safety concerns due to deploying suboptimal policies. In contrast, our method leverages human intervention data to implicitly overcome the transferring problem in a domain-agnostic way and also leads to safer deployment.

Human-in-The-Loop Robot Learning Human-in-the-loop machine learning is a prevalent framework to inject human knowledge into autonomous systems [62, 115, 116]. Various forms of human feedback exist [63], ranging from passive judgement, such as preference [117–126] and evaluation [127–132], to active involvement, including intervention [133–135] and correction [136, 137]. They are widely adopted in solutions for sequential decision-making tasks. For instance, interactive imitation learning [66, 67, 70, 138] leverages human intervention and correction to help na¨ıve imitators address data mismatch and compounding error. In the context of RL, reward functions can be derived to better align agent behaviors with human preferences [120, 123, 124, 127]. Noticeably, recent trend focuses on continually improving robots’ capability by iteratively updating and deploying policies with human feedback [70], combining active human involvement with RL [137], and autonomously generating corrective intervention data [139]. Our work further extends this trend by showing that sim-to-real gaps can be effectively eliminated by using human intervention and correction signals

In shared autonomy, robots and humans share the control authority to achieve a common goal [64, 65, 140–142]. This control paradigm has been largely studied in assistive robotics and human-robot collaboration [143–145]. In this work, we provide a novel perspective by employing it in sim-to-real transfer of robot control policies and demonstrating its importance in attaining effective transfer.

6 Conclusion and Limitations

In this work, we present TRANSIC, a holistic human-in-the-loop method to tackle sim-to-real transfer of policies for contact-rich manipulation tasks. We show that in sim-to-real transfer, a good base policy learned from the simulation can be combined with limited real-world data to achieve success. However, effectively utilizing human correction data to address the sim-to-real gap is challenging, especially when we want to prevent catastrophic forgetting of the base policy. TRANSIC successfully addresses these challenges by learning a gated residual policy from human correction data. We show that TRANSIC is effective as a holistic approach to address different types of sim-to-real gaps when presented simultaneously; it is also effective as an approach to address individual gaps of very different natures. It displays attractive properties, such as scaling with human effort.

While TRANSIC achieves remarkable sim-to-real transfer results, it still has several limitations which should be addressed by follow-up studies. 1) The current tasks are limited to the tabletop scenario with a soft parallel-jaw gripper. However, we notice that with the development of teleoperation devices for more complicated robots, such as bimanual arms [146], dexterous hands [147], mobile manipulators [148, 149], and humanoids [150], TRANSIC can also be potentially applied to these settings. 2) During the correction data collection phase, the human operator still manually decides when to intervene. This effort might be reduced by leveraging automatic failure detection techniques [151, 152]. 3) TRANSIC requires simulation policies with reasonable performances in the first place, which is challenging to learn by itself. Nevertheless, TRANSIC is compatible with and can benefit from recent advances in this direction [6, 85, 153].

Acknowledgments

We are grateful to Josiah Wong, Chengshu (Eric) Li, Weiyu Liu, Wenlong Huang, Stephen Tian, Sanjana Srivastava, and the SVL PAIR group for their helpful feedback and insightful discussions. This work is in part supported by the Stanford Institute for Human-Centered AI (HAI), ONR MURI N00014-22-1-2740, ONR MURI N00014-21-1-2801, and Schmidt Sciences. Ruohan Zhang is partially supported by Wu Tsai Human Performance Alliance Fellowship.

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