Hierarchical Planning in the Now

By Leslie Pack Kaelbling, Tomas Lozano-Perez. 2010

143 citations recorded by the source corpus.

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In LLM Agents & Tool Use

Citations in the corpus

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (2022, background): “Task and motion planning [59, 60] is a problem of sequencing tasks to solve a high-level problem, while ensuring the feasibility given an embodiment (task”
Inner Monologue: Embodied Reasoning through Planning with Language Models (2022, background): “Task and motion planning [22, 23] requires simultaneously solving a high-level, discrete task planning problem [24, 25, 26], and a low-level, continuous motion planning problem [27].”
When to use parametric models in reinforcement learning? (2019, background): “The ability to generalise to unseen or counter-factual transitions can be used to plan from the current state into the future (sometimes called planning ‘in the now’ [Kaelbling and Lozano-Pérez, 2010]), even if this exact state has never before been observed.”
Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image (2020, methodology): “A general approach to TAMP is to combine discrete logic search with a sampling-based motion planning algorithm [24, 7, 40, 6] or constraint satisfaction methods [27, 28, 31].”
ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation (2024, methodology): “For sequential manipulation tasks, task and motion planning (TAMP) [1, 8, 13] is a widely used framework, often formulated as constraint satisfaction problems [11, 75–80] with continuous geometric problems as subroutines.”
A Planning Framework for Non-Prehensile Manipulation under Clutter and Uncertainty (2012, unknown): “Recently, Kaelbling and Lozano-Perez (2011) proposed a planner that also identifies obstacles by computing swept volumes of future actions.”
Online Replanning in Belief Space for Partially Observable Task and Motion Problems (2019, background): “There is much work that addresses the problem of efficiently solving deterministic, fully-observable TAMP problems [1], [2], [3], [4], [5], [6].”
KnowRob: A knowledge processing infrastructure for cognition-enabled robots (2013, background): “(2010), planning of actions and motions (Wolfe et al., 2010; Kaelbling and Lozano-Perez, 2011), and the generation of robot controllers from logical constraints (Kress-Gazit et al.”
Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planning (2020, methodology): “These methods then plan over them, for example, via trees or optimization-based methods [11, 12, 13, 14, 15, 16, 17].”