Learning Robot Metareasoning: Adaptive Computation Budgets for Real-Time Control

Key points

• Planning while moving faces an historical trade-off between deliberation and reactivity.1
• A planner running concurrently with execution needs time to compute, so the robot must commit to an open-loop action chunk for that duration: a longer commitment buys a better plan at the cost of feedback frequency.1
• However, the optimal computation time is highly state-dependent (due to factors such as unmodeled dynamics, safety constraints, and marginal optimisation gains), i.e. dynamic, therefore offline static tuning risks leaving systems blind during critical maneuvers or unable to find global effective solutions.1
• To solve this, we train a reinforcement-learning policy conditioned on the current state, the executing plan, and inexpensive planner statistics.1

Sources (1)

• [1]Learning Robot Metareasoning: Adaptive Computation Budgets for Real-Time Control
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 03:55 PM

  We cast the identification of the optimal computation budget as a semi-Markov decision process (SMDP) in which a single decision defines both the robot action commitments and the time allocated to the solver.

  Planning while moving faces an historical trade-off between deliberation and reactivity.

Extractive summary: sentences quoted from the sources.