Learning a State Representation and Navigation in Cluttered and Dynamic Environments (2103.04351v1)

Abstract: In this work, we present a learning-based pipeline to realise local navigation with a quadrupedal robot in cluttered environments with static and dynamic obstacles. Given high-level navigation commands, the robot is able to safely locomote to a target location based on frames from a depth camera without any explicit mapping of the environment. First, the sequence of images and the current trajectory of the camera are fused to form a model of the world using state representation learning. The output of this lightweight module is then directly fed into a target-reaching and obstacle-avoiding policy trained with reinforcement learning. We show that decoupling the pipeline into these components results in a sample efficient policy learning stage that can be fully trained in simulation in just a dozen minutes. The key part is the state representation, which is trained to not only estimate the hidden state of the world in an unsupervised fashion, but also helps bridging the reality gap, enabling successful sim-to-real transfer. In our experiments with the quadrupedal robot ANYmal in simulation and in reality, we show that our system can handle noisy depth images, avoid dynamic obstacles unseen during training, and is endowed with local spatial awareness.

• The paper introduces a pipeline that fuses image sequences and camera trajectories to develop an efficient state representation for navigation.
• It employs reinforcement learning in simulation to train target-reaching and obstacle avoidance policies in approximately 12 minutes.
• Experimental results validate the system's robust sim-to-real transfer and effective dynamic obstacle avoidance, complementing traditional SLAM methods.

Learning a State Representation and Navigation in Cluttered and Dynamic Environments

In "Learning a State Representation and Navigation in Cluttered and Dynamic Environments," Hoeller et al. present an innovative approach to local navigation using legged robots in complex settings populated by static and dynamic obstacles. The central contribution of this paper is a learning-based pipeline designed to enable a quadrupedal robot, specifically ANYmal, to reach target locations safely using depth camera inputs without constructing explicit environmental maps.

Methodology

The researchers propose a sequence of stages starting with the fusion of image sequences and camera trajectory to develop a model of the world using state representation learning. This process results in a lightweight module producing a data-efficient representation that informs the subsequent target-reaching and obstacle-avoiding policy trained through reinforcement learning (RL). Notably, their approach allows for the entire policy learning phase to be conducted in simulation in approximately 12 minutes, emphasizing the module's sample efficiency.

A crucial aspect lies in the state representation's capability to provide unsupervised estimation of the world's hidden state. This feature significantly assists in bridging the reality gap, thereby enhancing successful sim-to-real transfer—an essential aspect for the deployment of learned navigation policies in real-world robotics contexts.

Experimental Results

The experiments conducted span simulations and real-world tests, validating this method's robustness and applicability. The results demonstrate the robot's competency in handling noisy depth images and navigating amid dynamic obstacles not encountered during training. Moreover, the approach is shown to complement SLAM-based methodologies, contributing additional capabilities for dynamic obstacle avoidance not conventionally addressed by mapping-focused techniques.

Implications

This work underscores the potential for integrating state representation learning and reinforcement learning strategies to tackle complex navigation challenges in robotics. The implications of this research extend to scenarios requiring agile navigation and obstacle avoidance in environments where traditional mapping approaches may be constrained by computation or real-time data processing demands.

The findings suggest future directions for deploying similar strategies in a broader range of robotic systems, potentially enhancing autonomous vehicles' adaptability and operational efficiency across varied terrain and environmental conditions.

Conclusion

The proposed pipeline represents a promising advance in robotic navigation within dynamic contexts, offering a scalable framework that leverages unsupervised learning and RL for effective local navigation. The paper also opens feasible pathways for sim-to-real deployment, marking a significant stride in the application of machine learning techniques to advanced robotic systems.

Overall, Hoeller et al.'s work provides meaningful insights into robot navigation technology, potentially enriching theoretical frameworks and facilitating practical developments in modern robotics. Future research may focus on refining the system's perception capabilities, exploring additional sensory modalities, or further improving policy robustness to enrich real-world applicability.