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3D-Anchored Lookahead Planning for Persistent Robotic Scene Memory via World-Model-Based MCTS

Authors
Bronislav Sidik, Dror Mizrahi
Source
arXiv
Published
Collection
Embodied AI
World Model PlanningMonte Carlo Tree SearchRobot Scene MemoryVision Language ActionEmbodied AI
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Summary

This paper proposes 3D-ALP, a test-time planning method for robot manipulation that keeps a persistent 3D camera pose memory and uses world-model rollouts with MCTS. The main claim is that this persistent 3D anchor fixes the failure of reactive vision-language-action policies on tasks where target objects leave the camera view.

Problem

  • Reactive VLA-style robot policies map the current image to an action, so they lose track of objects once those objects are occluded or out of frame.
  • That failure matters in multi-step manipulation, where the robot may need to revisit a location seen earlier or act on objects in a sequence after they are no longer visible.
  • The paper argues this is an architectural memory problem, not just a model-capacity problem: without an explicit scene memory state, the policy must guess.

Approach

  • 3D-ALP stores a persistent camera-to-world pose c2w ∈ SE(3) and updates it after each real action with forward kinematics, so visited locations remain addressable even after occlusion.
  • It uses a 3D-consistent world model, InSpatio-WorldFM, to render predicted views from candidate future c2w poses, then runs Monte Carlo Tree Search over these imagined states.
  • After each executed action, the MCTS tree is re-rooted instead of discarded, which preserves previously explored spatial states as memory.
  • A hybrid scorer combines a semantic image score with a geometric distance penalty so the planner does not trust 2D visual overlap when the gripper is still far away in 3D.
  • The method also adds four practical MCTS fixes for robot control: remove the zero-action trap, reset node depths after rerooting, backpropagate max value instead of mean, and set a much smaller UCB exploration constant (c = 0.02).

Results

  • On the E3 multi-step coherence task in MuJoCo with a Franka Panda, memory-task success rate is 0.650 ± 0.109 for 3D-ALP versus 0.006 ± 0.008 for the greedy reactive baseline, a +0.645 gain.
  • On the hardest chained-memory step (step 5), greedy gets 0.000 success, MCTS with depth D=1 gets 0.622, and 3D-ALP with D=2 gets 0.822, so deeper lookahead adds +0.200 on that step.
  • The paper states that on memory-required steps, the reactive baseline drops to about 0.6% success while 3D-ALP keeps about 65%; Figure 1 also reports step-5 performance of 82.2% versus 0.0% for greedy.
  • The ablation says persistent tree memory is the main source of the gain: D=1 MCTS reaches memory SR 0.539 ± 0.064, already far above greedy 0.006 ± 0.008.
  • On non-memory steps, greedy is stronger than shallow MCTS in the reported table: greedy 0.748 ± 0.029 versus 0.389 ± 0.024 for D=1 MCTS, which supports the paper's claim that the benefit is concentrated on occlusion-driven memory steps.
  • Component checks report perfect geometric consistency in simulation: SSIM = 1.000, ORB = 1.000 with 391/391 keypoints matched across 5 trajectories, plus kinematic bridge angular error 0.00°, 100% pass rate under a ≤5° threshold, and bridge latency 0.5 ms.

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