Autonomous Robots最新文献

This paper presents the methods used by team CSIRO Data61 for multi-agent coordination and exploration in the DARPA Subterranean (SubT) Challenge. The SubT competition involved a single operator sending teams of robots to rapidly explore underground environments with severe navigation and communication challenges. Coordination was framed as a multi-robot task allocation (MRTA) problem to allow for a seamless integration of exploration with other required tasks. Methods for extending a consensus-based task allocation approach for an online and highly dynamic mission are discussed. Exploration tasks were generated from frontiers in a map of traversable space, and graph-based heuristics applied to guide the selection of exploration tasks. Results from simulation, field testing, and the final competition are presented. Team CSIRO Data61 tied for most points scored and achieved second place during the final SubT event.

For a robot to personalize physical assistance effectively, it must learn user preferences that can be generally reapplied to future scenarios. In this work, we investigate personalization of household cleanup with robots that can tidy up rooms by picking up objects and putting them away. A key challenge is determining the proper place to put each object, as people’s preferences can vary greatly depending on personal taste or cultural background. For instance, one person may prefer storing shirts in the drawer, while another may prefer them on the shelf. We aim to build systems that can learn such preferences from just a handful of examples via prior interactions with a particular person. We show that robots can combine language-based planning and perception with the few-shot summarization capabilities of large language models to infer generalized user preferences that are broadly applicable to future interactions. This approach enables fast adaptation and achieves 91.2% accuracy on unseen objects in our benchmark dataset. We also demonstrate our approach on a real-world mobile manipulator called TidyBot, which successfully puts away 85.0% of objects in real-world test scenarios.

When robots perform long action sequences, users will want to easily and reliably find out what they have done. We therefore demonstrate the task of learning to summarize and answer questions about a robot agent’s past actions using natural language alone. A single system with a large language model at its core is trained to both summarize and answer questions about action sequences given ego-centric video frames of a virtual robot and a question prompt. To enable training of question answering, we develop a method to automatically generate English-language questions and answers about objects, actions, and the temporal order in which actions occurred during episodes of robot action in the virtual environment. Training one model to both summarize and answer questions enables zero-shot transfer of representations of objects learned through question answering to improved action summarization.

We propose Text2Motion, a language-based planning framework enabling robots to solve sequential manipulation tasks that require long-horizon reasoning. Given a natural language instruction, our framework constructs both a task- and motion-level plan that is verified to reach inferred symbolic goals. Text2Motion uses feasibility heuristics encoded in Q-functions of a library of skills to guide task planning with Large Language Models. Whereas previous language-based planners only consider the feasibility of individual skills, Text2Motion actively resolves geometric dependencies spanning skill sequences by performing geometric feasibility planning during its search. We evaluate our method on a suite of problems that require long-horizon reasoning, interpretation of abstract goals, and handling of partial affordance perception. Our experiments show that Text2Motion can solve these challenging problems with a success rate of 82%, while prior state-of-the-art language-based planning methods only achieve 13%. Text2Motion thus provides promising generalization characteristics to semantically diverse sequential manipulation tasks with geometric dependencies between skills. Qualitative results are made available at https://sites.google.com/stanford.edu/text2motion.

Many tasks require robots to manipulate objects while satisfying a complex interplay of spatial and temporal constraints. For instance, a table setting robot first needs to place a mug and then fill it with coffee, while satisfying spatial relations such as forks need to placed left of plates. We propose the spatio-temporal framework SpaTiaL that unifies the specification, monitoring, and planning of object-oriented robotic tasks in a robot-agnostic fashion. SpaTiaL is able to specify diverse spatial relations between objects and temporal task patterns. Our experiments with recorded data, simulations, and real robots demonstrate how SpaTiaL provides real-time monitoring and facilitates online planning. SpaTiaL is open source and easily expandable to new object relations and robotic applications.

We address multi-robot geometric task-and-motion planning (MR-GTAMP) problems in synchronous, monotone setups. The goal of the MR-GTAMP problem is to move objects with multiple robots to goal regions in the presence of other movable objects. We focus on collaborative manipulation tasks where the robots have to adopt intelligent collaboration strategies to be successful and effective, i.e., decide which robot should move which objects to which positions, and perform collaborative actions, such as handovers. To endow robots with these collaboration capabilities, we propose to first collect occlusion and reachability information for each robot by calling motion-planning algorithms. We then propose a method that uses the collected information to build a graph structure which captures the precedence of the manipulations of different objects and supports the implementation of a mixed-integer program to guide the search for highly effective collaborative task-and-motion plans. The search process for collaborative task-and-motion plans is based on a Monte-Carlo Tree Search (MCTS) exploration strategy to achieve exploration-exploitation balance. We evaluate our framework in two challenging MR-GTAMP domains and show that it outperforms two state-of-the-art baselines with respect to the planning time, the resulting plan length and the number of objects moved. We also show that our framework can be applied to underground mining operations where a robotic arm needs to coordinate with an autonomous roof bolter. We demonstrate plan execution in two roof-bolting scenarios both in simulation and on robots.

Autonomous robots are one of the critical components in modern manufacturing systems. For this reason, the uninterrupted operation of robots in manufacturing is important for the sustainability of autonomy. Detecting possible fault symptoms that may cause failures within a work environment will help to eliminate interrupted operations. When supervised learning methods are considered, obtaining and storing labeled, historical training data in a manufacturing environment with faults is a challenging task. In addition, sensors in mobile devices such as robots are exposed to different noisy external conditions in production environments affecting data labels and fault mapping. Furthermore, relying on a single sensor data for fault detection often causes false alarms for equipment monitoring. Our study takes requirements into consideration and proposes a new unsupervised machine-learning algorithm to detect possible operational faults encountered by autonomous mobile robots. The method suggests using an ensemble of multi-sensor information fusion at the decision level by voting to enhance decision reliability. The proposed technique relies on dissimilarity-based sensor data segmentation with an adaptive threshold control. It has been tested experimentally on an autonomous mobile robot. The experimental results show that the proposed method is effective for detecting operational anomalies. Furthermore, the proposed voting mechanism is also capable of eliminating false positives in case of a single source of information is utilized.