Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Migimatsu, Toki"'
Task and Motion Planning (TAMP) algorithms solve long-horizon robotics tasks by integrating task planning with motion planning; the task planner proposes a sequence of actions towards a goal state and the motion planner verifies whether this action s
Externí odkaz:
http://arxiv.org/abs/2405.08572
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
Externí odkaz:
http://arxiv.org/abs/2303.12153
Solving real-world manipulation tasks requires robots to have a repertoire of skills applicable to a wide range of circumstances. When using learning-based methods to acquire such skills, the key challenge is to obtain training data that covers diver
Externí odkaz:
http://arxiv.org/abs/2211.06134
Advances in robotic skill acquisition have made it possible to build general-purpose libraries of learned skills for downstream manipulation tasks. However, naively executing these skills one after the other is unlikely to succeed without accounting
Externí odkaz:
http://arxiv.org/abs/2210.12250
Robots deployed in human-centric environments may need to manipulate a diverse range of articulated objects, such as doors, dishwashers, and cabinets. Articulated objects often come with unexpected articulation mechanisms that are inconsistent with c
Externí odkaz:
http://arxiv.org/abs/2205.03721
Manipulation tasks often require a robot to adjust its sensorimotor skills based on the state it finds itself in. Taking peg-in-hole as an example: once the peg is aligned with the hole, the robot should push the peg downwards. While high level execu
Externí odkaz:
http://arxiv.org/abs/2203.02468
Autor:
Migimatsu, Toki, Bohg, Jeannette
Symbols representing abstract states such as "dish in dishwasher" or "cup on table" allow robots to reason over long horizons by hiding details unnecessary for high-level planning. Current methods for learning to identify symbolic states in visual da
Externí odkaz:
http://arxiv.org/abs/2109.14718
In this paper, we explore whether a robot can learn to hang arbitrary objects onto a diverse set of supporting items such as racks or hooks. Endowing robots with such an ability has applications in many domains such as domestic services, logistics, o
Externí odkaz:
http://arxiv.org/abs/2103.14283
Autor:
Migimatsu, Toki, Bohg, Jeannette
Publikováno v:
IEEE Robotics and Automation Letters (2020) vol. 5, issue 2, pp. 844-851
We address the problem of applying Task and Motion Planning (TAMP) in real world environments. TAMP combines symbolic and geometric reasoning to produce sequential manipulation plans, typically specified as joint-space trajectories, which are valid o
Externí odkaz:
http://arxiv.org/abs/1911.04679
Learning contact-rich, robotic manipulation skills is a challenging problem due to the high-dimensionality of the state and action space as well as uncertainty from noisy sensors and inaccurate motor control. To combat these factors and achieve more
Externí odkaz:
http://arxiv.org/abs/1911.00969