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A survey of optimization-based task and motion planning: From classical to learning approaches
Task and motion planning (TAMP) integrates high-level task planning and low-level motion
planning to equip robots with the autonomy to effectively reason over long-horizon, dynamic …
planning to equip robots with the autonomy to effectively reason over long-horizon, dynamic …
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 …
isdf: Real-time neural signed distance fields for robot perception
We present iSDF, a continual learning system for real-time signed distance field (SDF)
reconstruction. Given a stream of posed depth images from a moving camera, it trains a …
reconstruction. Given a stream of posed depth images from a moving camera, it trains a …
NeuralFeels with neural fields: Visuotactile perception for in-hand manipulation
To achieve human-level dexterity, robots must infer spatial awareness from multimodal
sensing to reason over contact interactions. During in-hand manipulation of novel objects …
sensing to reason over contact interactions. During in-hand manipulation of novel objects …
Learning multi-object dynamics with compositional neural radiance fields
We present a method to learn compositional multi-object dynamics models from image
observations based on implicit object encoders, Neural Radiance Fields (NeRFs), and …
observations based on implicit object encoders, Neural Radiance Fields (NeRFs), and …
Reinforcement learning with neural radiance fields
It is a long-standing problem to find effective representations for training reinforcement
learning (RL) agents. This paper demonstrates that learning state representations with …
learning (RL) agents. This paper demonstrates that learning state representations with …
Imitating task and motion planning with visuomotor transformers
Imitation learning is a powerful tool for training robot manipulation policies, allowing them to
learn from expert demonstrations without manual programming or trial-and-error. However …
learn from expert demonstrations without manual programming or trial-and-error. However …
PRIF: primary ray-based implicit function
We introduce a new implicit shape representation called Primary Ray-based Implicit
Function (PRIF). In contrast to most existing approaches based on the signed distance …
Function (PRIF). In contrast to most existing approaches based on the signed distance …
Neural grasp distance fields for robot manipulation
We formulate grasp learning as a neural field and present Neural Grasp Distance Fields
(NGDF). Here, the input is a 6D pose of a robot end effector and output is a distance to a …
(NGDF). Here, the input is a 6D pose of a robot end effector and output is a distance to a …
DFields: Dynamic 3D Descriptor Fields for Zero-Shot Generalizable Robotic Manipulation
Scene representation has been a crucial design choice in robotic manipulation systems. An
ideal representation should be 3D, dynamic, and semantic to meet the demands of diverse …
ideal representation should be 3D, dynamic, and semantic to meet the demands of diverse …