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8th Conference on robot learning, CoRL 2024

作者： Shridhar, Mohit Lo, Yat Long James, Stephen Dyson Robot Learning Lab United Kingdom

Image-generation diffusion models have been fine-tuned to unlock new capabilities such as image-editing and novel view synthesis. Can we similarly unlock image-generation models for visuomotor control? We present GENIMA, a behavior-cloning agent that fine-tunes Stable Diffusion to "draw joint-actions" as targets on RGB images. These images are fed into a controller that maps the visual targets into a sequence of joint-positions. We study GENIMA on 25 RLBench and 9 real-world manipulation tasks. We find that, by lifting actions into image-space, internet pre-trained diffusion models can generate policies that outperform state-of-the-art visuomotor approaches, especially in robustness to scene perturbations and generalizing to novel objects. Our method is also competitive with 3D agents, despite lacking priors such as depth, keypoints, or motion-planners. © 2024 Proceedings of Machine learning Research.

关键词： Clone cells

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Large-Scale Autonomous Flight With Real-Time Semantic SLAM Under Dense Forest Canopy

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IEEE robotICS AND AUTOMATION LETTERS 2022年第2期7卷 5512-5519页

作者： Liu, Xu Nardari, Guilherme, V Ojeda, Fernando Cladera Tao, Yuezhan Zhou, Alex Donnelly, Thomas Qu, Chao Chen, Steven W. Romero, Roseli A. F. Taylor, Camillo J. Kumar, Vijay Univ Penn GRASP Lab Philadelphia PA 19104 USA Univ Sao Paulo Robot Learning Lab Sao Paulo SP Brazil

Semantic maps represent the environment using a set of semantically meaningful objects. This representation is storage-efficient, less ambiguous, and more informative, thus facilitating large-scale autonomy and the acquisition of actionable information in highly unstructured, GPS-denied environments. In this letter, we propose an integrated system that can perform large-scale autonomous flights and real-time semantic mapping in challenging under-canopy environments. We detect and model tree trunks and ground planes from LiDAR data, which are associated across scans and used to constrain robot poses as well as tree trunk models. The autonomous navigation module utilizes a multi-level planning and mapping framework and computes dynamically feasible trajectories that lead the UAV to build a semantic map of the user-defined region of interest in a computationally and storage efficient manner. A drift-compensation mechanism is designed to minimize the odometry drift using semantic SLAM outputs in real time, while maintaining planner optimality and controller stability. This leads the UAV to execute its mission accurately and safely at scale.

关键词： Aerial systems: perception and autonomy field robotics robotics and automation in agriculture and forestry SLAM

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MoGaze: A Dataset of Full-Body Motions that Includes Workspace Geometry and Eye-Gaze

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IEEE robotICS AND AUTOMATION LETTERS 2021年第2期6卷 367-373页

作者： Kratzer, Philipp Bihlmaier, Simon Midlagajni, Niteesh Balachandra Prakash, Rohit Toussaint, Marc Mainprice, Jim Univ Stuttgart Machine Learning & Robot Lab Stuttgart Germany Univ Stuttgart Humans Robots Mot Res Grp D-70174 Stuttgart Germany TU Learning & Intelligent Syst Lab Berlin Germany

As robots become more present in open human environments, it will become crucial for robotic systems to understand and predict human motion. Such capabilities depend heavily on the quality and availability of motion capture data. However, existing datasets of full-body motion rarely include 1) long sequences of manipulation tasks, 2) the 3D model of the workspace geometry, and 3) eye-gaze, which are all important when a robot needs to predict the movements of humans in close proximity. Hence, in this letter, we present a novel dataset of full-body motion for everyday manipulation tasks, which includes the above. The motion data was captured using a traditional motion capture system based on reflective markers. We additionally captured eye-gaze using a wearable pupil-tracking device. As we show in experiments, the dataset can be used for the design and evaluation of full-body motion prediction algorithms. Furthermore, our experiments show eye-gaze as a powerful predictor of human intent. The dataset includes 180 min of motion capture data with 1627 pick and place actions being performed. It is available at https://***/mogaze/ MoGaze, Dataset and is planned to be extended to collaborative tasks with two humans in the near future.

关键词： Datasets for human motion human-centered robotics modeling and simulating humans

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Sim-to-Real Transfer for Optical Tactile Sensing

Sim-to-Real Transfer for Optical Tactile Sensing

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IEEE International Conference on robotics and Automation (ICRA)

作者： Ding, Zihan Lepora, Nathan F. Johns, Edward Imperial Coll London Robot Learning Lab London England Univ Bristol Bristol Robot Lab Bristol Avon England

ISBN: (纸本)9781728173955

Deep learning and reinforcement learning methods have been shown to enable learning of flexible and complex robot controllers. However, the reliance on large amounts of training data often requires data collection to be carried out in simulation, with a number of sim-to-real transfer methods being developed in recent years. In this paper, we study these techniques for tactile sensing using the TacTip optical tactile sensor, which consists of a deformable tip with a camera observing the positions of pins inside this tip. We designed a model for soft body simulation which was implemented using the Unity physics engine, and trained a neural network to predict the locations and angles of edges when in contact with the sensor. Using domain randomisation techniques for sim-to-real transfer, we show how this framework can be used to accurately predict edges with less than 1 mm prediction error in real-world testing, without any real-world data at all.

关键词： Forecasting

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Control-Tree Optimization: an approach to MPC under discrete Partial Observability

Control-Tree Optimization: an approach to MPC under discrete...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Phiquepal, Camille Toussaint, Marc Univ Stuttgart Machine Learning & Robot Lab Stuttgart Germany TU Berlin Learning & Intelligent Syst Lab Berlin Germany

ISBN: (纸本)9781728190778

This paper presents a new approach to Model Predictive Control for environments where essential, discrete variables are partially observed. Under this assumption, the belief state is a probability distribution over a finite number of states. We optimize a control-tree where each branch assumes a given state-hypothesis. The control-tree optimization uses the probabilistic belief state information. This leads to policies more optimized with respect to likely states than unlikely ones, while still guaranteeing robust constraint satisfaction at all times. We apply the method to both linear and non-linear MPC with constraints. The optimization of the control-tree is decomposed into optimization subproblems that are solved in parallel leading to good scalability for high number of state-hypotheses. We demonstrate the real-time feasibility of the algorithm on two examples and show the benefits compared to a classical MPC scheme optimizing w.r.t. one single hypothesis.

关键词： Automation Scalability Conferences Probabilistic logic Real-time systems Probability distribution Observability

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Path-Tree Optimization in Discrete Partially Observable Environments Using Rapidly-Exploring Belief-Space Graphs

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IEEE robotICS AND AUTOMATION LETTERS 2022年第4期7卷 10160-10167页

作者： Phiquepal, Camille Orthey, Andreas Viennot, Nicolas Toussaint, Marc Univ Stuttgart Machine Learning & Robot Lab D-80807 Munich Germany TU Berlin Learning & Intelligent Syst Lab D-10587 Berlin Germany Columbia Univ Software Syst Lab New York NY 10027 USA

robots often need to solve path planning problems where essential and discrete aspects of the environment are partially observable. This introduces a multi-modality, where the robot must be able to observe and infer the state of its environment. To tackle this problem, we introduce the Path-Tree Optimization (PTO) algorithm which plans a path-tree in belief-space. A path-tree is a tree-like motion with branching points where the robot receives an observation leading to a belief-state update. The robot takes different branches depending on the observation received. The algorithm assumes a deterministic observation model and is composed of three main steps. First, a rapidly-exploring random graph (RRG) on the state space is grown. Second, the RRG is expanded to a belief-space graph by querying the observation model. In a third step, dynamic programming is performed on the belief-space graph to extract a path-tree. The resulting path-tree combines exploration with exploitation i.e. it balances the need for gaining knowledge about the environment with the need for reaching the goal. We demonstrate the algorithm capabilities on navigation and mobile manipulation tasks, and show its advantage over a baseline using a task and motion planning approach (TAMP) both in terms of optimality and runtime.

关键词： Motion and path planning planning under uncertainty task and motion planning

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DALL-E-Bot: Introducing Web-Scale Diffusion Models to robotics

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IEEE robotICS AND AUTOMATION LETTERS 2023年第7期8卷 3956-3963页

作者： Kapelyukh, Ivan Vosylius, Vitalis Johns, Edward Imperial Coll London Robot Learning Lab London SW7 2AZ England Imperial Coll London Dyson Robot Lab London SW7 2AZ England

We introduce the first work to explore web-scale diffusion models for robotics. DALL-E-Bot enables a robot to rearrange objects in a scene, by first inferring a text description of those objects, then generating an image representing a natural, human-like arrangement of those objects, and finally physically arranging the objects according to that goal image. We show that this is possible zero-shot using DALL-E, without needing any further example arrangements, data collection, or training. DALL-E-Bot is fully autonomous and is not restricted to a pre-defined set of objects or scenes, thanks to DALL-E's web-scale pre-training. Encouraging real-world results, with both human studies and objective metrics, show that integrating web-scale diffusion models into robotics pipelines is a promising direction for scalable, unsupervised robot learning.

关键词： AI-based methods Big Data in robotics and automation deep learning in grasping and manipulation

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learning Communication for Cooperation in Dynamic Agent-Number Environment

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2021年第4期26卷 1846-1857页

作者： Liu, Weiwei Liu, Shanqi Cao, Junjie Wang, Qi Lang, Xiaolei Liu, Yong Zhejiang Univ Coll Control Sci & Engn Adv Percept Robot & Intelligent Learning Lab Hangzhou 310027 Peoples R China

The number of agents in many multiagent systems in the real world, such as storage robots and drone cluster systems, continually changes. Still, most current multiagent reinforcement learning (RL) algorithms are limited to fixed network dimensions, and prior knowledge is used to preset the number of agents in the training phase, which leads to a poor generalization of the algorithm. In addition, these algorithms use centralized training to solve the instability problem of multiagent systems. However, the centralized learning of large-scale multiagent RL algorithms will lead to an explosion of network dimensions, which in turn leads to very limited scalability of centralized learning algorithms. To solve these two difficulties, in this article propose a group centralized training and decentralized execution-unlimited dynamic agent-number network (GCTDE-UDAN). First, since we use the attention mechanism to select several leaders and establish a dynamic number of teams, and the UDAN performs a nonlinear combination of all agents' Q values when performing value decomposition, it is not affected by changes in the number of agents. Moreover, our algorithm can unite any agent to form a group and conduct centralized training within the group, avoiding network dimension explosion caused by the global centralized training of large-scale agents. Finally, we verified on the simulation and experimental platform that the algorithm can learn and perform cooperative behaviors in many dynamic multiagent environments.

关键词： Training Reinforcement learning Heuristic algorithms Task analysis Mechatronics IEEE transactions Multi-agent systems Attention mechanism multiagent reinforcement learning (RL) multiagent system recurrent neural network (RNN)

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Online Motion Planning Over Multiple Homotopy Classes with Gaussian Process Inference

Online Motion Planning Over Multiple Homotopy Classes with G...

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IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Kolur, Keshav Chintalapudi, Sahit Boots, Byron Mukadam, Mustafa Georgia Inst Technol Robot Learning Lab Atlanta GA 30332 USA

ISBN: (纸本)9781728140049

Efficient planning in dynamic and uncertain environments is a fundamental challenge in robotics. In the context of trajectory optimization, the feasibility of paths can change as the environment evolves. Therefore, it can be beneficial to reason about multiple possible paths simultaneously. We build on prior work that considers graph-based trajectories to find solutions in multiple homotopy classes concurrently. Specifically, we extend this previous work to an online setting where the unreachable (in time) part of the graph is pruned and the remaining graph is reoptimized at every time step. As the robot moves within the graph on the path that is most promising, the pruning and reoptimization allows us to retain candidate paths that may become more viable in the future as the environment changes, essentially enabling the robot to dynamically switch between numerous homotopy classes. We compare our approach against prior work without the homotopy switching capability and show improved performance across several metrics in simulation with a 2D robot in multiple dynamic environments under noisy measurements and execution.

关键词： Motion planning

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Efficient Motion Planning Based on Kinodynamic Model for Quadruped robots Following Persons in Confined Spaces

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2021年第4期26卷 1997-2006页

作者： Zhang, Zhen Yan, Jiaqing Kong, Xin Zhai, Guangyao Liu, Yong Zhejiang Univ Coll Control Sci & Engn Adv Percept Robot & Intelligent Learning Lab Hangzhou 310027 Peoples R China

Quadruped robots have superior terrain adaptability and flexible movement capabilities than traditional robots. In this article, we innovatively apply it in person-following tasks, and propose an efficient motion planning scheme for quadruped robots to generate a flexible and effective trajectory in confined spaces. The method builds a real-time local costmap via onboard sensors, which involves both static and dynamic obstacles. And we exploit a simplified kinodynamic model and formulate the friction pyramids formed by ground reaction forces' inequality constraints to ensure the executable of the optimized trajectory. In addition, we obtain the optimal following trajectory in the costmap completely based on the robot's rectangular footprint description, which ensures that it can walk through the narrow spaces avoiding collision. Finally, a receding horizon control strategy is employed to improve the robustness of motion in complex environments. The proposed motion planning framework is integrated on the quadruped robot JueYing and tested in simulation as well as real scenarios. It shows that the execution success rates in various scenes are all over 90%.

关键词： robots Planning robot sensing systems Trajectory Legged locomotion Three-dimensional displays robot kinematics Indoor navigation Mobile robots Motion planning robot kinematics

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