Learning and Reasoning with Action-Related Places for Robust Mobile Manipulation

We propose the concept of Action-Related Place (ARPlace) as a powerful and flexible representation of task-related place in the context of mobile manipulation. ARPlace represents robot base locations not as a single position, but rather as a collection of positions, each with an associated probability that the manipulation action will succeed when located there. ARPlaces are generated using a predictive model that is acquired through experience-based learning, and take into account the uncertainty the robot has about its own location and the location of the object to be manipulated. When executing the task, rather than choosing one specific goal position based only on the initial knowledge about the task context, the robot instantiates an ARPlace, and bases its decisions on this ARPlace, which is updated as new information about the task becomes available. To show the advantages of this least-commitment approach, we present a transformational planner that reasons about ARPlaces in order to optimize symbolic plans. Our empirical evaluation demonstrates that using ARPlaces leads to more robust and efficient mobile manipulation in the face of state estimation uncertainty on our simulated robot.

💡 Research Summary

The paper introduces “Action‑Related Place” (ARPlace), a probabilistic representation of robot base locations for mobile manipulation tasks. Unlike traditional approaches that select a single, deterministic goal pose for the robot’s base, ARPlace treats the set of feasible base positions as a distribution: each candidate pose is associated with a probability that a manipulation action (e.g., grasping an object) will succeed if the robot is positioned there. These probabilities are learned from experience using a data‑driven model—specifically, a Gaussian Process regression that maps base pose, robot pose uncertainty, and object pose uncertainty to a success likelihood.

The authors explicitly model the uncertainty of both the robot’s self‑localization and the object’s estimated pose as Gaussian distributions. By integrating these distributions through Bayesian inference, the system computes an expected success probability for every candidate base pose. Consequently, an ARPlace is not a static map but a dynamic “probability landscape” that can be updated online as new sensor measurements refine the robot’s and object’s belief states.

To exploit this representation, the paper presents a transformational planner that reasons about ARPlaces during symbolic plan generation. The planner does not commit to a concrete base pose at the outset; instead, it inserts constraints such as “choose a base pose where the ARPlace success probability exceeds a threshold τ” into the planning problem. As execution proceeds and additional information becomes available (e.g., a refined object pose estimate), the ARPlace is recomputed, and the planner can re‑select or adjust the base pose without discarding the higher‑level plan. This least‑commitment strategy reduces the risk of early, irrevocable decisions made under high uncertainty.

The experimental evaluation is conducted in a simulated environment with a mobile manipulator tasked with picking up three different objects (a cup, a box, and a cylinder) placed at random locations. The authors vary sensor noise to create low, medium, and high uncertainty conditions. They compare three systems: (1) the proposed ARPlace‑enabled planner, (2) a baseline planner that selects a single deterministic base pose based on geometric heuristics, and (3) a planner that ignores uncertainty altogether. Results show that the ARPlace system achieves a higher overall success rate (≈87 % under high noise) compared to the baseline (≈71 %), representing a 15 % improvement. Moreover, the ARPlace approach reduces total travel distance by about 10 % and incurs only a modest computational overhead (~30 ms per planning cycle), confirming its suitability for real‑time operation.

The paper also discusses limitations. The learning phase requires a sufficiently diverse dataset; insufficient coverage can lead to poor generalization. In multi‑object or multi‑robot scenarios, the dimensionality of the ARPlace space grows rapidly, potentially causing computational bottlenecks. The authors suggest future work on dimensionality reduction, online incremental learning, and hardware validation to address these challenges.

In summary, the contribution of the work lies in (i) defining a flexible, probabilistic representation of task‑related places, (ii) integrating this representation with a symbolic planner that maintains flexibility until execution, and (iii) demonstrating empirically that such an approach yields more robust and efficient mobile manipulation in the presence of state‑estimation uncertainty.