When arranging objects with robotic arms, the quality of the end result strongly depends on the achievable placement accuracy. However, even the most advanced robotic systems are prone to positioning errors that can occur at different steps of the manipulation process. Ignoring such errors can lead to the partial or complete failure of the arrangement. In this paper, we present a novel approach to autonomously detect and correct misplaced objects by pushing them with a robotic arm. We thoroughly tested our approach both in simulation and on real hardware using a Robotiq two-finger gripper mounted on a UR5 robotic arm. In our evaluation, we demonstrate the successful compensation for different errors injected during the manipulation of regular shaped objects. Consequently, we achieve a highly reliable object placement accuracy in the millimeter range.
相關內容
Efficient and accurate segmentation of unseen objects is crucial for robotic manipulation. However, it remains challenging due to over- or under-segmentation. Although existing refinement methods can enhance the segmentation quality, they fix only minor boundary errors or are not sufficiently fast. In this work, we propose INSTAnce Boundary Explicit Error Estimation and Refinement (INSTA-BEEER), a novel refinement model that allows for adding and deleting instances and sharpening boundaries. Leveraging an error-estimation-then-refinement scheme, the model first estimates the pixel-wise boundary explicit errors: true positive, true negative, false positive, and false negative pixels of the instance boundary in the initial segmentation. It then refines the initial segmentation using these error estimates as guidance. Experiments show that the proposed model significantly enhances segmentation, achieving state-of-the-art performance. Furthermore, with a fast runtime (less than 0.1 s), the model consistently improves performance across various initial segmentation methods, making it highly suitable for practical robotic applications.
Despite significant advancements in existing models, generating text descriptions from structured data input, known as data-to-text generation, remains a challenging task. In this paper, we propose a novel approach that goes beyond traditional one-shot generation methods by introducing a multi-step process consisting of generation, verification, and correction stages. Our approach, VCP(Verification and Correction Prompting), begins with the model generating an initial output. We then proceed to verify the correctness of different aspects of the generated text. The observations from the verification step are converted into a specialized error-indication prompt, which instructs the model to regenerate the output while considering the identified errors. To enhance the model's correction ability, we have developed a carefully designed training procedure. This procedure enables the model to incorporate feedback from the error-indication prompt, resulting in improved output generation. Through experimental results, we demonstrate that our approach effectively reduces slot error rates while maintaining the overall quality of the generated text.
The ability to detect and analyze failed executions automatically is crucial for an explainable and robust robotic system. Recently, Large Language Models (LLMs) have demonstrated strong common sense reasoning skills on textual inputs. To leverage the power of LLM for robot failure explanation, we propose a framework REFLECT, which converts multi-sensory data into a hierarchical summary of robot past experiences and queries LLM with a progressive failure explanation algorithm. Conditioned on the explanation, a failure correction planner generates an executable plan for the robot to correct the failure and complete the task. To systematically evaluate the framework, we create the RoboFail dataset and show that our LLM-based framework is able to generate informative failure explanations that assist successful correction planning. Project website: //roboreflect.github.io/
We present a new reproducible benchmark for evaluating robot manipulation in the real world, specifically focusing on pick-and-place. Our benchmark uses the YCB objects, a commonly used dataset in the robotics community, to ensure that our results are comparable to other studies. Additionally, the benchmark is designed to be easily reproducible in the real world, making it accessible to researchers and practitioners. We also provide our experimental results and analyzes for model-based and model-free 6D robotic grasping on the benchmark, where representative algorithms are evaluated for object perception, grasping planning, and motion planning. We believe that our benchmark will be a valuable tool for advancing the field of robot manipulation. By providing a standardized evaluation framework, researchers can more easily compare different techniques and algorithms, leading to faster progress in developing robot manipulation methods.
Real-time learning is crucial for robotic agents adapting to ever-changing, non-stationary environments. A common setup for a robotic agent is to have two different computers simultaneously: a resource-limited local computer tethered to the robot and a powerful remote computer connected wirelessly. Given such a setup, it is unclear to what extent the performance of a learning system can be affected by resource limitations and how to efficiently use the wirelessly connected powerful computer to compensate for any performance loss. In this paper, we implement a real-time learning system called the Remote-Local Distributed (ReLoD) system to distribute computations of two deep reinforcement learning (RL) algorithms, Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO), between a local and a remote computer. The performance of the system is evaluated on two vision-based control tasks developed using a robotic arm and a mobile robot. Our results show that SAC's performance degrades heavily on a resource-limited local computer. Strikingly, when all computations of the learning system are deployed on a remote workstation, SAC fails to compensate for the performance loss, indicating that, without careful consideration, using a powerful remote computer may not result in performance improvement. However, a carefully chosen distribution of computations of SAC consistently and substantially improves its performance on both tasks. On the other hand, the performance of PPO remains largely unaffected by the distribution of computations. In addition, when all computations happen solely on a powerful tethered computer, the performance of our system remains on par with an existing system that is well-tuned for using a single machine. ReLoD is the only publicly available system for real-time RL that applies to multiple robots for vision-based tasks.
Tactile perception is a crucial sensing modality in robotics, particularly in scenarios that require precise manipulation and safe interaction with other objects. Previous research in this area has focused extensively on tactile perception of contact poses as this is an important capability needed for tasks such as traversing an object's surface or edge, manipulating an object, or pushing an object along a predetermined path. Another important capability needed for tasks such as object tracking and manipulation is estimation of post-contact shear but this has received much less attention. Indeed, post-contact shear has often been considered a "nuisance variable" and is removed if possible because it can have an adverse effect on other types of tactile perception such as contact pose estimation. This paper proposes a tactile robotic system that can simultaneously estimate both the contact pose and post-contact shear, and use this information to control its interaction with other objects. Moreover, our new system is capable of interacting with other objects in a smooth and continuous manner, unlike the stepwise, position-controlled systems we have used in the past. We demonstrate the capabilities of our new system using several different controller configurations, on tasks including object tracking, surface following, single-arm object pushing, and dual-arm object pushing.
While we can see robots in more areas of our lives, they still make errors. One common cause of failure stems from the robot perception module when detecting objects. Allowing users to correct such errors can help improve the interaction and prevent the same errors in the future. Consequently, we investigate the effectiveness of a virtual reality (VR) framework for correcting perception errors of a Franka Panda robot. We conducted a user study with 56 participants who interacted with the robot using both VR and screen interfaces. Participants learned to collaborate with the robot faster in the VR interface compared to the screen interface. Additionally, participants found the VR interface more immersive, enjoyable, and expressed a preference for using it again. These findings suggest that VR interfaces may offer advantages over screen interfaces for human-robot interaction in erroneous environments.
This paper presents a decentralized leader-follower multi-robot formation control based on a reinforcement learning (RL) algorithm applied to a swarm of small educational Sphero robots. Since the basic Q-learning method is known to require large memory resources for Q-tables, this work implements the Double Deep Q-Network (DDQN) algorithm, which has achieved excellent results in many robotic problems. To enhance the system behavior, we trained two different DDQN models, one for reaching the formation and the other for maintaining it. The models use a discrete set of robot motions (actions) to adapt the continuous nonlinear system to the discrete nature of RL. The presented approach has been tested in simulation and real experiments which show that the multi-robot system can achieve and maintain a stable formation without the need for complex mathematical models and nonlinear control laws.
The small size, high dexterity, and intrinsic compliance of continuum robots (CRs) make them well suited for constrained environments. Solving the inverse kinematics (IK), that is finding robot joint configurations that satisfy desired position or pose queries, is a fundamental challenge in motion planning, control, and calibration for any robot structure. For CRs, the need to avoid obstacles in tightly confined workspaces greatly complicates the search for feasible IK solutions. Without an accurate initialization or multiple re-starts, existing algorithms often fail to find a solution. We present CIDGIKc (Convex Iteration for Distance-Geometric Inverse Kinematics for Continuum Robots), an algorithm that solves these nonconvex feasibility problems with a sequence of semidefinite programs whose objectives are designed to encourage low-rank minimizers. CIDGIKc is enabled by a novel distance-geometric parameterization of constant curvature segment geometry for CRs with extensible segments. The resulting IK formulation involves only quadratic expressions and can efficiently incorporate a large number of collision avoidance constraints. Our experimental results demonstrate >98% solve success rates within complex, highly cluttered environments which existing algorithms cannot account for.
Collecting and leveraging data with good coverage properties plays a crucial role in different aspects of reinforcement learning (RL), including reward-free exploration and offline learning. However, the notion of "good coverage" really depends on the application at hand, as data suitable for one context may not be so for another. In this paper, we formalize the problem of active coverage in episodic Markov decision processes (MDPs), where the goal is to interact with the environment so as to fulfill given sampling requirements. This framework is sufficiently flexible to specify any desired coverage property, making it applicable to any problem that involves online exploration. Our main contribution is an instance-dependent lower bound on the sample complexity of active coverage and a simple game-theoretic algorithm, CovGame, that nearly matches it. We then show that CovGame can be used as a building block to solve different PAC RL tasks. In particular, we obtain a simple algorithm for PAC reward-free exploration with an instance-dependent sample complexity that, in certain MDPs which are "easy to explore", is lower than the minimax one. By further coupling this exploration algorithm with a new technique to do implicit eliminations in policy space, we obtain a computationally-efficient algorithm for best-policy identification whose instance-dependent sample complexity scales with gaps between policy values.
注冊地址： 北京市海淀區羊坊店路18號2幢3層301-191