We propose a programming paradigm for robotics that has the potential to drastically facilitate robotic programming. Building up on Sikuli, a GUI automation language, we abstract specific robotic perception and contro...
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ISBN:
(纸本)9781479946051
We propose a programming paradigm for robotics that has the potential to drastically facilitate robotic programming. Building up on Sikuli, a GUI automation language, we abstract specific robotic perception and control capabilities into first-class objects that are embedded in a simple scripting language. Currently, robotics programming requires a deep understanding of perception, controls and algorithms, knowledge of a specific robot's perception capabilities and kinematics, and finally a substantial amount of software engineering. Although learn-by-demonstration allows also relatively unskilled users to adapt a robot to their needs, this approach is intrinsically limited by the complexity such a program can reach. This paper presents a proof-of-concept for migrating Sikuli from the virtual GUI workspace of computer software to the physical 3D workspace of robotics. It then presents an example use case that illustrates the power of this new approach using a simple script that arranges a set of randomly aligned blocks into a tower using a Baxter robot equipped with an Asus Xtion Pro.
Path planning in a changing environment is a challenging task in robotics, as moving objects impose time-dependent constraints. Recent planning methods primarily focus on the spatial aspects, lacking the capability to...
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ISBN:
(纸本)9798350384581;9798350384574
Path planning in a changing environment is a challenging task in robotics, as moving objects impose time-dependent constraints. Recent planning methods primarily focus on the spatial aspects, lacking the capability to directly incorporate time constraints. In this paper, we propose a method that leverages a generative model to decompose a complex planning problem into small manageable ones by incrementally generating subgoals given the current planning context. Then, we take into account the temporal information and use learned time estimators based on different statistic distributions to examine and select the generated subgoal candidates. Experiments show that planning from the current robot state to the selected subgoal can satisfy the given time-dependent constraints while being goal-oriented.
Planning for contact-rich manipulation involves discontinuous dynamics, which presents challenges to planning methods. Sampling-based planners have higher sample complexity in high-dimensional problems and cannot effi...
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ISBN:
(纸本)9798350377712;9798350377705
Planning for contact-rich manipulation involves discontinuous dynamics, which presents challenges to planning methods. Sampling-based planners have higher sample complexity in high-dimensional problems and cannot efficiently handle state constraints such as force limits. Gradient-based solvers can suffer from local optima and their convergence rate is often worse on non-smooth problems. We propose a planning method that is both sampling- and gradient-based, using the Cross-entropy Method to initialize a gradient-based solver, providing better initialization to the gradient-based method and allowing explicit handling of state constraints. The sampling-based planner also allows direct integration of a particle filter, which is here used for online contact mode estimation. The approach is shown to improve performance in MuJoCo environments and the effects of problem stiffness and planing horizon are investigated. The estimator and planner are then applied to an impedance-controlled robot, showing a reduction in solve time in contact transitions to only gradient-based.
robots need to have a memory of previously observed, but currently occluded objects to work reliably in realistic environments. We investigate the problem of encoding object-oriented memory into a multi-object manipul...
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ISBN:
(纸本)9798350384581;9798350384574
robots need to have a memory of previously observed, but currently occluded objects to work reliably in realistic environments. We investigate the problem of encoding object-oriented memory into a multi-object manipulation reasoning and planning framework. We propose DOOM and LOOM, which leverage transformer relational dynamics to encode the history of trajectories given partial-view point clouds and an object discovery and tracking engine. Our approaches can perform multiple challenging tasks including reasoning with occluded objects, novel objects appearance, and object reappearance. Throughout our extensive simulation and real-world experiments, we find that our approaches perform well in terms of different numbers of objects and different numbers of distractor actions. Furthermore, we show our approaches outperform an implicit memory baseline.
To operate safely and efficiently, autonomous warehouse/delivery robots must be able to accomplish tasks while navigating in dynamic environments and handling the large uncertainties associated with the motions/behavi...
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ISBN:
(纸本)9798350377712;9798350377705
To operate safely and efficiently, autonomous warehouse/delivery robots must be able to accomplish tasks while navigating in dynamic environments and handling the large uncertainties associated with the motions/behaviors of other robots and/or humans. A key scenario in such environments is the hallway problem, where robots must operate in the same narrow corridor as human traffic going in one or both directions. Traditionally, robot planners have tended to focus on socially acceptable behavior in the hallway scenario at the expense of performance. This paper proposes a planner that aims to address the consequent "robot freezing problem" in hallways by allowing for "peek-and-pass" maneuvers. We then go on to demonstrate in simulation how this planner improves robot time to goal without violating social norms. Finally, we show initial hardware demonstrations of this planner in the real world, along with a novel STAR (Socially Trained Agile robot) platform designed with human comfort in mind.
We study a variant of the multi-robot goal assignment problem where a unique goal to each robot needs to be assigned while minimizing the largest cost of movement among the robots, called makespan. A significant step ...
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ISBN:
(纸本)1577358872
We study a variant of the multi-robot goal assignment problem where a unique goal to each robot needs to be assigned while minimizing the largest cost of movement among the robots, called makespan. A significant step in solving this problem is to find the cost associated with the robot-goal pairs, which requires solving a complex path planning problem. We present OM, a scalable optimal algorithm that solves the multi-robot goal assignment problem by computing the paths for a significantly less number of robot-goal pairs compared to the state-of-the-art algorithms, leading to a computationally superior mechanism to solve the problem. We extensively evaluate our algorithm for hundreds of robots on randomly generated and standard workspaces. Our experimental results demonstrate that the proposed algorithm achieves a noticeable speedup over two state-of-the-art baseline algorithms.
Guided trajectory planning involves a leader robot strategically directing a follower robot to collaboratively reach a designated destination. However, this task becomes notably challenging when the leader lacks compl...
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ISBN:
(纸本)9798350384581;9798350384574
Guided trajectory planning involves a leader robot strategically directing a follower robot to collaboratively reach a designated destination. However, this task becomes notably challenging when the leader lacks complete knowledge of the follower's decision-making model. There is a need for learning-based methods to effectively design the cooperative plan. To this end, we develop a Stackelberg game-theoretic approach based on the Koopman operator to address the challenge. We first formulate the guided trajectory planning problem through the lens of a dynamic Stackelberg game. We then leverage Koopman operator theory to acquire a learning-based linear system model that approximates the follower's feedback dynamics. Based on this learned model, the leader devises a collision-free trajectory to guide the follower using receding horizon planning. We use simulations to elaborate on the effectiveness of our approach in generating learning models that accurately predict the follower's multi-step behavior when compared to alternative learning techniques. Moreover, our approach successfully accomplishes the guidance task and notably reduces the leader's planning time to nearly half when contrasted with the model-based baseline method(1).
Manipulating deformable objects remains a challenge within robotics due to the difficulties of state estimation, long-horizon planning, and predicting how the object will deform given an interaction. These challenges ...
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ISBN:
(纸本)9798350377712;9798350377705
Manipulating deformable objects remains a challenge within robotics due to the difficulties of state estimation, long-horizon planning, and predicting how the object will deform given an interaction. These challenges are the most pronounced with 3D deformable objects. We propose SculptDiff, a goal-conditioned diffusion-based imitation learning framework that works with point cloud state observations to directly learn clay sculpting policies for a variety of target shapes. To the best of our knowledge this is the first real-world method that successfully learns manipulation policies for 3D deformable objects. For sculpting videos and access to our dataset and hardware CAD models, see the project website: https://***/***/imitation-sculpting/home
Generalizable articulated object manipulation is essential for home-assistant robots. Recent efforts focus on imitation learning from demonstrations or reinforcement learning in simulation, however, due to the prohibi...
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ISBN:
(纸本)9798350384581;9798350384574
Generalizable articulated object manipulation is essential for home-assistant robots. Recent efforts focus on imitation learning from demonstrations or reinforcement learning in simulation, however, due to the prohibitive costs of real-world data collection and precise object simulation, it still remains challenging for these works to achieve broad adaptability across diverse articulated objects. Recently, many works have tried to utilize the strong in-context learning ability of Large Language Models (LLMs) to achieve generalizable robotic manipulation, but most of these researches focus on high-level task planning, sidelining low-level robotic control. In this work, building on the idea that the kinematic structure of the object determines how we can manipulate it, we propose a kinematic-aware prompting framework that prompts LLMs with kinematic knowledge of objects to generate low-level motion trajectory waypoints, supporting various object manipulation. To effectively prompt LLMs with the kinematic structure of different objects, we design a unified kinematic knowledge parser, which represents various articulated objects as a unified textual description containing kinematic joints and contact location. Building upon this unified description, a kinematic-aware planner model is proposed to generate precise 3D manipulation waypoints via a designed kinematic-aware chain-of-thoughts prompting method. Our evaluation spanned 48 instances across 16 distinct categories, revealing that our framework not only outperforms traditional methods on 8 seen categories but also shows a powerful zero-shot capability for 8 unseen articulated object categories with only 17 demonstrations. Moreover, the real-world experiments on 7 different object categories prove our framework's adaptability in practical scenarios. Code is released at https://***/GeWu-Lab/LLM_articulated_object_manipulation.
We present progress on the problem of reconfiguring a 2D arrangement of building material by a cooperative group of robots. These robots must avoid collisions, deadlocks, and are subjected to the constraint of maintai...
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ISBN:
(纸本)9798350384581;9798350384574
We present progress on the problem of reconfiguring a 2D arrangement of building material by a cooperative group of robots. These robots must avoid collisions, deadlocks, and are subjected to the constraint of maintaining connectivity of the structure. We develop two reconfiguration methods, one based on spatio-temporal planning, and one based on target swapping, to increase building efficiency. The first method can significantly reduce planning times compared to other multi-robot planners. The second method helps to reduce the amount of time robots spend waiting for paths to be cleared, and the overall distance traveled by the robots.
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