ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning

We present a framework for intuitive robot programming by non-experts, leveraging natural language prompts and contextual information from the Robot Operating System (ROS). Our system integrates large language models (LLMs), enabling non-experts to articulate task requirements to the system through a chat interface. Key features of the framework include: integration of ROS with an AI agent connected to a plethora of open-source and commercial LLMs, automatic extraction of a behavior from the LLM output and execution of ROS actions/services, support for three behavior modes (sequence, behavior tree, state machine), imitation learning for adding new robot actions to the library of possible actions, and LLM reflection via human and environment feedback. Extensive experiments validate the framework, showcasing robustness, scalability, and versatility in diverse scenarios, including long-horizon tasks, tabletop rearrangements, and remote supervisory control. To facilitate the adoption of our framework and support the reproduction of our results, we have made our code open-source. You can access it at: https://github.com/huawei-noah/HEBO/tree/master/ROSLLM.

Multi-Objective Global Path Planning for Lunar Exploration With a Quadruped Robot

In unstructured environments the best path is not always the shortest, but needs to consider various objectives like energy efficiency, risk of failure or scientific outcome. This paper proposes a global planner, based on the A* algorithm, capable of individually considering multiple layers of map data for different cost objectives. We introduce weights between the objectives, which can be adapted to achieve a variety of optimal paths. In order to find the best of these paths, a tool for statistical path analysis is presented. Our planner was tested on exemplary lunar topographies to propose two trajectories for exploring the Aristarchus Plateau. The optimized paths significantly reduce the risk of failure while yielding more scientific value compared to a manually planned paths in the same area. The planner and analysis tool are made open-source in order to simplify mission planning for planetary scientists.

Dynamic Throwing with Robotic Material Handling Machines

Automation of hydraulic material handling machinery is currently limited to semi-static pick-and-place cycles. Dynamic throwing motions which utilize the passive joints, can greatly improve time efficiency as well as increase the dumping workspace. In this work, we use Reinforcement Learning (RL) to design dynamic controllers for material handlers with underactuated arms as commonly used in logistics. The controllers are tested both in simulation and in real-world experiments on a 12-ton test platform. The method is able to exploit the passive joints of the gripper to perform dynamic throwing motions. With the proposed controllers, the machine is able to throw individual objects to targets outside the static reachability zone with good accuracy for its practical applications. The work demonstrates the possibility of using RL to perform highly dynamic tasks with heavy machinery, suggesting a potential for improving the efficiency and precision of autonomous material handling tasks.

Motion Primitives Planning For Center-Articulated Vehicles

Autonomous navigation across unstructured terrains, including forests and construction areas, faces unique challenges due to intricate obstacles and the element of the unknown. Lacking pre-existing maps, these scenarios necessitate a motion planning approach that combines agility with efficiency. Critically, it must also incorporate the robot's kinematic constraints to navigate more effectively through complex environments. This work introduces a novel planning method for center-articulated vehicles (CAV), leveraging motion primitives within a receding horizon planning framework using onboard sensing. The approach commences with the offline creation of motion primitives, generated through forward simulations that reflect the distinct kinematic model of center-articulated vehicles. These primitives undergo evaluation through a heuristic-based scoring function, facilitating the selection of the most suitable path for real-time navigation. To augment this planning process, we develop a pose-stabilizing controller, tailored to the kinematic specifications of center-articulated vehicles. During experiments, our method demonstrates a $67\%$ improvement in SPL (Success Rate weighted by Path Length) performance over existing strategies. Furthermore, its efficacy was validated through real-world experiments conducted with a tree harvester vehicle - SAHA.

Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers

Legged locomotion has recently achieved remarkable success with the progress of machine learning techniques, especially deep reinforcement learning (RL). Controllers employing neural networks have demonstrated empirical and qualitative robustness against real-world uncertainties, including sensor noise and external perturbations. However, formally investigating the vulnerabilities of these locomotion controllers remains a challenge. This difficulty arises from the requirement to pinpoint vulnerabilities across a long-tailed distribution within a high-dimensional, temporally sequential space. As a first step towards quantitative verification, we propose a computational method that leverages sequential adversarial attacks to identify weaknesses in learned locomotion controllers. Our research demonstrates that, even state-of-the-art robust controllers can fail significantly under well-designed, low-magnitude adversarial sequence. Through experiments in simulation and on the real robot, we validate our approach's effectiveness, and we illustrate how the results it generates can be used to robustify the original policy and offer valuable insights into the safety of these black-box policies.

Cybathlon -- Legged Mobile Assistance for Quadriplegics

Assistance robots are the future for people who need daily care due to limited mobility or being wheelchair-bound. Current solutions of attaching robotic arms to motorized wheelchairs only provide limited additional mobility at the cost of increased size. We present a mouth joystick control interface, augmented with voice commands, for an independent quadrupedal assistance robot with an arm. We validate and showcase our system in the Cybathlon Challenges February 2024 Assistance Robot Race, where we solve four everyday tasks in record time, winning first place. Our system remains generic and sets the basis for a platform that could help and provide independence in the everyday lives of people in wheelchairs.

Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots

Autonomous wheeled-legged robots have the potential to transform logistics systems, improving operational efficiency and adaptability in urban environments. Navigating urban environments, however, poses unique challenges for robots, necessitating innovative solutions for locomotion and navigation. These challenges include the need for adaptive locomotion across varied terrains and the ability to navigate efficiently around complex dynamic obstacles. This work introduces a fully integrated system comprising adaptive locomotion control, mobility-aware local navigation planning, and large-scale path planning within the city. Using model-free reinforcement learning (RL) techniques and privileged learning, we develop a versatile locomotion controller. This controller achieves efficient and robust locomotion over various rough terrains, facilitated by smooth transitions between walking and driving modes. It is tightly integrated with a learned navigation controller through a hierarchical RL framework, enabling effective navigation through challenging terrain and various obstacles at high speed. Our controllers are integrated into a large-scale urban navigation system and validated by autonomous, kilometer-scale navigation missions conducted in Zurich, Switzerland, and Seville, Spain. These missions demonstrate the system's robustness and adaptability, underscoring the importance of integrated control systems in achieving seamless navigation in complex environments. Our findings support the feasibility of wheeled-legged robots and hierarchical RL for autonomous navigation, with implications for last-mile delivery and beyond.

Autonomous Forest Inventory with Legged Robots: System Design and Field Deployment

We present a solution for autonomous forest inventory with a legged robotic platform. Compared to their wheeled and aerial counterparts, legged platforms offer an attractive balance of endurance and low soil impact for forest applications. In this paper, we present the complete system architecture of our forest inventory solution which includes state estimation, navigation, mission planning, and real-time tree segmentation and trait estimation. We present preliminary results for three campaigns in forests in Finland and the UK and summarize the main outcomes, lessons, and challenges. Our UK experiment at the Forest of Dean with the ANYmal D legged platform, achieved an autonomous survey of a 0.96 hectare plot in 20 min, identifying over 100 trees with typical DBH accuracy of 2 cm.

Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision

Natural environments such as forests and grasslands are challenging for robotic navigation because of the false perception of rigid obstacles from high grass, twigs, or bushes. In this work, we present Wild Visual Navigation (WVN), an online self-supervised learning system for visual traversability estimation. The system is able to continuously adapt from a short human demonstration in the field, only using onboard sensing and computing. One of the key ideas to achieve this is the use of high-dimensional features from pre-trained self-supervised models, which implicitly encode semantic information that massively simplifies the learning task. Further, the development of an online scheme for supervision generator enables concurrent training and inference of the learned model in the wild. We demonstrate our approach through diverse real-world deployments in forests, parks, and grasslands. Our system is able to bootstrap the traversable terrain segmentation in less than 5 min of in-field training time, enabling the robot to navigate in complex, previously unseen outdoor terrains. Code: https://bit.ly/498b0CV - Project page:https://bit.ly/3M6nMHH

Learning Quadrupedal Locomotion via Differentiable Simulation

The emergence of differentiable simulators enabling analytic gradient computation has motivated a new wave of learning algorithms that hold the potential to significantly increase sample efficiency over traditional Reinforcement Learning (RL) methods. While recent research has demonstrated performance gains in scenarios with comparatively smooth dynamics and, thus, smooth optimization landscapes, research on leveraging differentiable simulators for contact-rich scenarios, such as legged locomotion, is scarce. This may be attributed to the discontinuous nature of contact, which introduces several challenges to optimizing with analytic gradients. The purpose of this paper is to determine if analytic gradients can be beneficial even in the face of contact. Our investigation focuses on the effects of different soft and hard contact models on the learning process, examining optimization challenges through the lens of contact simulation. We demonstrate the viability of employing analytic gradients to learn physically plausible locomotion skills with a quadrupedal robot using Short-Horizon Actor-Critic (SHAC), a learning algorithm leveraging analytic gradients, and draw a comparison to a state-of-the-art RL algorithm, Proximal Policy Optimization (PPO), to understand the benefits of analytic gradients.