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 …

The motivation behind our work is to review and analyze the most relevant studies on deep
reinforcement learning-based object manipulation. Various studies are examined through a …

Decision-making is challenging in robotics environments with continuous object-centric
states, continuous actions, long horizons, and sparse feedback. Hierarchical approaches …

We present a visually grounded hierarchical planning algorithm for long-horizon
manipulation tasks. Our algorithm offers a joint framework of neuro-symbolic task planning …

Imitation learning from human demonstrations can teach robots complex manipulation skills,
but is time-consuming and labor intensive. In contrast, Task and Motion Planning (TAMP) …

Robotic planning problems in hybrid state and action spaces can be solved by integrated
task and motion planners (TAMP) that handle the complex interaction between motion-level …

The objective of this work is to augment the basic abilities of a robot by learning to use
sensorimotor primitives to solve complex long-horizon manipulation problems. This requires …

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 …

Task and motion planning (TMP) combines discrete search and continuous motion planning.
Earlier work has shown that to efficiently find a task-motion plan, the discrete search can …

In robotic domains, learning and planning are complicated by continuous state spaces,
continuous action spaces, and long task horizons. In this work, we address these challenges …