Vision-Guided Quadrupedal Locomotion in the Wild with Multi-Modal Delay Randomization (2109.14549v2)

Abstract: Developing robust vision-guided controllers for quadrupedal robots in complex environments, with various obstacles, dynamical surroundings and uneven terrains, is very challenging. While Reinforcement Learning (RL) provides a promising paradigm for agile locomotion skills with vision inputs in simulation, it is still very challenging to deploy the RL policy in the real world. Our key insight is that aside from the discrepancy in the domain gap, in visual appearance between the simulation and the real world, the latency from the control pipeline is also a major cause of difficulty. In this paper, we propose Multi-Modal Delay Randomization (MMDR) to address this issue when training RL agents. Specifically, we simulate the latency of real hardware by using past observations, sampled with randomized periods, for both proprioception and vision. We train the RL policy for end-to-end control in a physical simulator without any predefined controller or reference motion, and directly deploy it on the real A1 quadruped robot running in the wild. We evaluate our method in different outdoor environments with complex terrains and obstacles. We demonstrate the robot can smoothly maneuver at a high speed, avoid the obstacles, and show significant improvement over the baselines. Our project page with videos is at https://mehooz.github.io/mmdr-wild/.

• The paper introduces MMDR, a technique that simulates sensor delays to bridge the sim-to-real gap in quadrupedal locomotion.
• It employs an end-to-end reinforcement learning framework that integrates visual and proprioceptive data for controlling robot actuators.
• Outdoor evaluations demonstrate that MMDR significantly enhances traversal distances and reduces collision rates compared to baseline methods.

Vision-Guided Quadrupedal Locomotion in the Wild with Multi-Modal Delay Randomization

The paper "Vision-Guided Quadrupedal Locomotion in the Wild with Multi-Modal Delay Randomization" presents an approach to overcoming significant challenges in deploying reinforcement learning (RL) policies for quadrupedal robots in real-world environments. This research focuses on addressing the inherent difficulties posed by asynchronous multi-modal observations due to sensor and system latencies. The proposed method, Multi-Modal Delay Randomization (MMDR), simulates these real-world conditions during RL policy training in a simulated environment, ultimately facilitating the direct deployment of trained policies onto physical robots without further adjustments.

Key Contributions

1. Multi-Modal Delay Randomization (MMDR): The central innovation presented is the MMDR technique, which accounts for asynchronous delays in proprioceptive and visual inputs. By simulating these delays during training, this approach bridges the sim-to-real gap effectively, enabling robust policy transfer to real-world conditions.
2. End-to-End Reinforcement Learning: The research advocates for a unified RL framework that integrates visual and proprioceptive data to directly control the joint actuators of a quadruped robot. This stands in contrast to hierarchical approaches, simplifying the architecture and enhancing robustness in diverse and unpredictable terrains.
3. Evaluation in Complex Environments: The robustness and efficacy of the MMDR-trained policies were evaluated in various outdoor environments featuring static and dynamic obstacles as well as complex terrains. The results demonstrated successful navigation and obstacle avoidance, with a noteworthy performance improvement over baseline models.

Numerical Results

The experimental outcomes underscore the efficacy of MMDR. The proposed approach significantly improved moving distance and reduced collision occurrences in both simulated and real-world environments. In simulation, MMDR-enhanced policies achieved longer traversal distances and fewer collisions than standard frameworks, highlighting the method's ability to generalize beyond the training domain.

Practical and Theoretical Implications

From a practical perspective, this work provides a methodology to train quadrupedal robots for deployment in dynamic, real-world environments straight from simulation. This could have substantial implications for applications such as search and rescue, where navigating unstructured terrains with visual cues is critical.

Theoretically, it addresses a longstanding challenge in robotics concerning the sim-to-real transfer of RL policies. By explicitly modeling the asynchronous nature of various sensory inputs, MMDR aligns the simulation more closely with reality, enhancing the transferability of learned behaviors.

Future Directions

Future research can explore extending the MMDR framework to integrate additional sensory modalities such as haptic feedback or auditory signals, potentially increasing the adaptability of robots in even more demanding environments. Additionally, leveraging this method in other robotic platforms—like drones or wheeled robots—may unveil new challenges and insights, fostering further advancements in autonomous navigation and machine learning.

In conclusion, this research offers a comprehensive solution to a critical issue in the deployment of RL policies for vision-guided locomotion, promising enhanced performances in complex and unpredictable real-world scenarios.