23 research outputs found
Integrated robot task and motion planning in belief space
In this paper, we describe an integrated strategy for planning, perception, state-estimation and action in complex mobile manipulation domains. The strategy is based on planning in the belief space of probability distribution over states. Our planning approach is based on hierarchical goal regression (pre-image back-chaining). We develop a vocabulary of fluents that describe sets of belief states, which are goals and subgoals in the planning process. We show that a relatively small set of symbolic operators lead to task-oriented perception in support of the manipulation goals. An implementation of this method is demonstrated in simulation and on a real PR2 robot, showing robust, flexible solution of mobile manipulation problems with multiple objects and substantial uncertainty.This work was supported in part by the NSF under Grant No. 1117325. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. We also gratefully acknowledge support from ONR MURI grant N00014-09-1-1051, from AFOSR grant AOARD-104135 and from the Singapore Ministry of Education under a grant to the Singapore-MIT International Design Center. We thank Willow Garage for the use of the PR2 robot as part of the PR2 Beta Program
Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation
Many real-world manipulation tasks consist of a series of subtasks that are
significantly different from one another. Such long-horizon, complex tasks
highlight the potential of dexterous hands, which possess adaptability and
versatility, capable of seamlessly transitioning between different modes of
functionality without the need for re-grasping or external tools. However, the
challenges arise due to the high-dimensional action space of dexterous hand and
complex compositional dynamics of the long-horizon tasks. We present Sequential
Dexterity, a general system based on reinforcement learning (RL) that chains
multiple dexterous policies for achieving long-horizon task goals. The core of
the system is a transition feasibility function that progressively finetunes
the sub-policies for enhancing chaining success rate, while also enables
autonomous policy-switching for recovery from failures and bypassing redundant
stages. Despite being trained only in simulation with a few task objects, our
system demonstrates generalization capability to novel object shapes and is
able to zero-shot transfer to a real-world robot equipped with a dexterous
hand. More details and video results could be found at
https://sequential-dexterity.github.ioComment: CoRL 202
Integrated Robot Task and Motion Planning in the Now
This paper provides an approach to integrating geometric motion planning with logical task planning for long-horizon tasks in domains with many objects. We propose a tight integration between the logical and geometric aspects of planning. We use a logical representation which includes entities that refer to poses, grasps, paths and regions, without the need for a priori discretization. Given this representation and some simple mechanisms for geometric inference, we characterize the pre-conditions and effects of robot actions in terms of these logical entities. We then reason about the interaction of the geometric and non-geometric aspects of our domains using the general-purpose mechanism of goal regression (also known as pre-image backchaining). We propose an aggressive mechanism for temporal hierarchical decomposition, which postpones the pre-conditions of actions to create an abstraction hierarchy that both limits the lengths of plans that need to be generated and limits the set of objects relevant to each plan. We describe an implementation of this planning method and demonstrate it in a simulated kitchen environment in which it solves problems that require approximately 100 individual pick or place operations for moving multiple objects in a complex domain.This work was supported in part by the NSF under Grant No. 1117325. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. We also gratefully acknowledge support from ONR MURI grant N00014-09-1-1051, from AFOSR grant AOARD-104135 and from
the Singapore Ministry of Education under a grant to the Singapore-MIT International Design Center. We thank Willow Garage for the use of the PR2 robot as part of the PR2 Beta Program
Embodied object schemas for grounding language use
Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 139-146).This thesis presents the Object Schema Model (OSM) for grounded language interaction. Dynamic representations of objects are used as the central point of coordination between actions, sensations, planning, and language use. Objects are modeled as object schemas -- sets of multimodal, object-directed behavior processes -- each of which can make predictions, take actions, and collate sensations, in the modalities of touch, vision, and motor control. This process-centered view allows the system to respond continuously to real-world activity, while still viewing objects as stabilized representations for planning and speech interaction. The model can be described from four perspectives, each organizing and manipulating behavior processes in a different way. The first perspective views behavior processes like thread objects, running concurrently to carry out their respective functions. The second perspective organizes the behavior processes into object schemas. The third perspective organizes the behavior processes into plan hierarchies to coordinate actions. The fourth perspective creates new behavior processes in response to language input.(cont.) Results from interactions with objects are used to update the object schemas, which then influence subsequent plans and actions. A continuous planning algorithm examines the current object schemas to choose between candidate processes according to a set of primary motivations, such as responding to collisions, exploring objects, and interacting with the human. An instance of the model has been implemented using a physical robotic manipulator. The implemented system is able to interpret basic speech acts that relate to perception of, and actions upon, objects in the robot's physical environment.by Kai-yuh Hsiao.Ph.D
Robotic manipulation of multiple objects as a POMDP
This paper investigates manipulation of multiple unknown objects in a crowded
environment. Because of incomplete knowledge due to unknown objects and
occlusions in visual observations, object observations are imperfect and action
success is uncertain, making planning challenging. We model the problem as a
partially observable Markov decision process (POMDP), which allows a general
reward based optimization objective and takes uncertainty in temporal evolution
and partial observations into account. In addition to occlusion dependent
observation and action success probabilities, our POMDP model also
automatically adapts object specific action success probabilities. To cope with
the changing system dynamics and performance constraints, we present a new
online POMDP method based on particle filtering that produces compact policies.
The approach is validated both in simulation and in physical experiments in a
scenario of moving dirty dishes into a dishwasher. The results indicate that:
1) a greedy heuristic manipulation approach is not sufficient, multi-object
manipulation requires multi-step POMDP planning, and 2) on-line planning is
beneficial since it allows the adaptation of the system dynamics model based on
actual experience
Planning Under Uncertainty in the Continuous Domain: A Generalized Belief Space Approach
© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.DOI: 10.1109/ICRA.2014.6907858This work investigates the problem of planning
under uncertainty, with application to mobile robotics. We
propose a probabilistic framework in which the robot bases
its decisions on the
generalized belief
, which is a probabilistic
description of its own state and of external variables of interest.
The approach naturally leads to a dual-layer architecture: an
inner estimation layer, which performs inference to predict the
outcome of possible decisions, and an
outer decisional layer
which is in charge of deciding the best action to undertake.
The approach does not discretize the state or control space,
and allows planning in continuous domain. Moreover, it allows
to relax the assumption of
maximum likelihood observations: predicted measurements are treated as random variables and
are not considered as
given. Experimental results show that
our planning approach produces smooth trajectories while
maintaining uncertainty within reasonable bounds
On Probabilistic Strategies for Robot Tasks
Robots must act purposefully and successfully in an uncertain world. Sensory information is inaccurate or noisy, actions may have a range of effects, and the robot's environment is only partially and imprecisely modeled. This thesis introduces active randomization by a robot, both in selecting actions to execute and in focusing on sensory information to interpret, as a basic tool for overcoming uncertainty. An example of randomization is given by the strategy of shaking a bin containing a part in order to orient the part in a desired stable state with some high probability. Another example consists of first using reliable sensory information to bring two parts close together, then relying on short random motions to actually mate the two parts, once the part motions lie below the available sensing resolution. Further examples include tapping parts that are tightly wedged, twirling gears before trying to mesh them, and vibrating parts to facilitate a mating operation