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 …
number of objects, taking actions to move itself through the world as well as to change the …
A survey on deep learning and deep reinforcement learning in robotics with a tutorial on deep reinforcement learning
This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …
applied to robotics. Both tools have been shown to be successful in delivering data-driven …
Voxposer: Composable 3d value maps for robotic manipulation with language models
Large language models (LLMs) are shown to possess a wealth of actionable knowledge that
can be extracted for robot manipulation in the form of reasoning and planning. Despite the …
can be extracted for robot manipulation in the form of reasoning and planning. Despite the …
Text2motion: From natural language instructions to feasible plans
Abstract We propose Text2Motion, a language-based planning framework enabling robots
to solve sequential manipulation tasks that require long-horizon reasoning. Given a natural …
to solve sequential manipulation tasks that require long-horizon reasoning. Given a natural …
Rekep: Spatio-temporal reasoning of relational keypoint constraints for robotic manipulation
Representing robotic manipulation tasks as constraints that associate the robot and the
environment is a promising way to encode desired robot behaviors. However, it remains …
environment is a promising way to encode desired robot behaviors. However, it remains …
Grounded decoding: Guiding text generation with grounded models for robot control
Recent progress in large language models (LLMs) has demonstrated the ability to learn and
leverage Internet-scale knowledge through pre-training with autoregressive models …
leverage Internet-scale knowledge through pre-training with autoregressive models …
Deep affordance foresight: Planning through what can be done in the future
Planning in realistic environments requires searching in large planning spaces. Affordances
are a powerful concept to simplify this search, because they model what actions can be …
are a powerful concept to simplify this search, because they model what actions can be …
Grounded decoding: Guiding text generation with grounded models for embodied agents
Recent progress in large language models (LLMs) has demonstrated the ability to learn and
leverage Internet-scale knowledge through pre-training with autoregressive models …
leverage Internet-scale knowledge through pre-training with autoregressive models …
Deep visual reasoning: Learning to predict action sequences for task and motion planning from an initial scene image
In this paper, we propose a deep convolutional recurrent neural network that predicts action
sequences for task and motion planning (TAMP) from an initial scene image. Typical TAMP …
sequences for task and motion planning (TAMP) from an initial scene image. Typical TAMP …
Learning to solve sequential physical reasoning problems from a scene image
In this article, we propose deep visual reasoning, which is a convolutional recurrent neural
network that predicts discrete action sequences from an initial scene image for sequential …
network that predicts discrete action sequences from an initial scene image for sequential …