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Found 33 papers, 9 papers with code
Embodied Uncertainty-Aware Object Segmentation
To deal with uncertainty in robot perception, we propose a method for generating a hypothesis distribution of object segmentation.
Trust the PRoC3S: Solving Long-Horizon Robotics Problems with LLMs and Constraint Satisfaction
Recent developments in pretrained large language models (LLMs) applied to robotics have demonstrated their capacity for sequencing a set of discrete skills to achieve open-ended goals in simple robotic tasks.
Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness
We propose a strategy for TAMP with Uncertainty and Risk Awareness (TAMPURA) that is capable of efficiently solving long-horizon planning problems with initial-state and action outcome uncertainty, including problems that require information gathering and avoiding undesirable and irreversible outcomes.
Practice Makes Perfect: Planning to Learn Skill Parameter Policies
We consider a setting where a robot is initially equipped with (1) a library of parameterized skills, (2) an AI planner for sequencing together the skills given a goal, and (3) a very general prior distribution for selecting skill parameters.
What Planning Problems Can A Relational Neural Network Solve?
Goal-conditioned policies are generally understood to be "feed-forward" circuits, in the form of neural networks that map from the current state and the goal specification to the next action to take.
Learning Reusable Manipulation Strategies
Humans demonstrate an impressive ability to acquire and generalize manipulation "tricks."
Compositional Diffusion-Based Continuous Constraint Solvers
This paper introduces an approach for learning to solve continuous constraint satisfaction problems (CCSP) in robotic reasoning and planning.
Embodied Lifelong Learning for Task and Motion Planning
A robot deployed in a home over long stretches of time faces a true lifelong learning problem.
DiMSam: Diffusion Models as Samplers for Task and Motion Planning under Partial Observability
Task and Motion Planning (TAMP) approaches are effective at planning long-horizon autonomous robot manipulation.
PDSketch: Integrated Planning Domain Programming and Learning
This paper studies a model learning and online planning approach towards building flexible and general robots.
Learning Rational Subgoals from Demonstrations and Instructions
We present a framework for learning useful subgoals that support efficient long-term planning to achieve novel goals.
Sequence-Based Plan Feasibility Prediction for Efficient Task and Motion Planning
The core of our algorithm is PIGINet, a novel Transformer-based learning method that takes in a task plan, the goal, and the initial state, and predicts the probability of finding motion trajectories associated with the task plan.
Learning Efficient Abstract Planning Models that Choose What to Predict
An effective approach to solving long-horizon tasks in robotics domains with continuous state and action spaces is bilevel planning, wherein a high-level search over an abstraction of an environment is used to guide low-level decision-making.
Representation, learning, and planning algorithms for geometric task and motion planning
The first is an algorithm for learning a rank function that guides the discrete task level search, and the second is an algorithm for learning a sampler that guides the continuous motionlevel search.
Long-Horizon Manipulation of Unknown Objects via Task and Motion Planning with Estimated Affordances
We present a strategy for designing and building very general robot manipulation systems involving the integration of a general-purpose task-and-motion planner with engineered and learned perception modules that estimate properties and affordances of unknown objects.
Active Learning of Abstract Plan Feasibility
Long horizon sequential manipulation tasks are effectively addressed hierarchically: at a high level of abstraction the planner searches over abstract action sequences, and when a plan is found, lower level motion plans are generated.
Planning for Multi-stage Forceful Manipulation
The robot must choose a sequence of discrete actions, or strategy, such as whether to pick up an object, and the continuous parameters of each of those actions, such as how to grasp the object.
Robotics
Integrated Task and Motion Planning
The problem of planning for a robot that operates in environments containing a large number of objects, taking actions to move itself through the world as well as to change the state of the objects, is known as task and motion planning (TAMP).
Learning compositional models of robot skills for task and motion planning
We use, and develop novel improvements on, state-of-the-art methods for active learning and sampling.
Visual Prediction of Priors for Articulated Object Interaction
Given a novel object, the objective is to maximize reward with few interactions.
Scalable and Probabilistically Complete Planning for Robotic Spatial Extrusion
There is increasing demand for automated systems that can fabricate 3D structures.
Online Replanning in Belief Space for Partially Observable Task and Motion Problems
To solve multi-step manipulation tasks in the real world, an autonomous robot must take actions to observe its environment and react to unexpected observations.
Learning Compact Models for Planning with Exogenous Processes
We address the problem of approximate model minimization for MDPs in which the state is partitioned into endogenous and (much larger) exogenous components.
Learning Quickly to Plan Quickly Using Modular Meta-Learning
Multi-object manipulation problems in continuous state and action spaces can be solved by planners that search over sampled values for the continuous parameters of operators.
Modular meta-learning
Many prediction problems, such as those that arise in the context of robotics, have a simplifying underlying structure that, if known, could accelerate learning.
Learning What Information to Give in Partially Observed Domains
We consider such a setting in which the agent can, while acting, transmit declarative information to the human that helps them understand aspects of this unseen environment.
Active model learning and diverse action sampling for task and motion planning
Solving long-horizon problems in complex domains requires flexible generative planning that can combine primitive abilities in novel combinations to solve problems as they arise in the world.
Integrating Human-Provided Information Into Belief State Representation Using Dynamic Factorization
In partially observed environments, it can be useful for a human to provide the robot with declarative information that represents probabilistic relational constraints on properties of objects in the world, augmenting the robot's sensory observations.
PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning
We extend PDDL to support a generic, declarative specification for these procedures that treats their implementation as black boxes.
STRIPS Planning in Infinite Domains
We introduce STRIPStream: an extension of the STRIPS language which can model these domains by supporting the specification of blackbox generators to handle complex constraints.
Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems
We introduce a framework for model learning and planning in stochastic domains with continuous state and action spaces and non-Gaussian transition models.
Bayesian Optimization with Exponential Convergence
This paper presents a Bayesian optimization method with exponential convergence without the need of auxiliary optimization and without the delta-cover sampling.
Object-based World Modeling in Semi-Static Environments with Dependent Dirichlet-Process Mixtures
We refer to this attribute-based representation as a world model, and consider how to acquire it via noisy perception and maintain it over time, as objects are added, changed, and removed in the world.
