Papers for

robotics system designers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Learning-guided planning improves mobile sensor charging decisions

Learning-Guided Planning in Large Dynamic Action Spaces: Budgeted Tree Search for One-to-Many Mobile Charging

Abstract: Many learned sequential decision systems map the current state directly to an action. That shortcut becomes brittle when candidate actions are numerous, geometrically structured, and rebuilt with the state. One-to-many mobile charging makes this setting concrete: with N=250 sensors, the initial state induces about 1,125 candidate charging-stop actions; each chosen stop simultaneously serves its in-range sensors, and the action universe changes as sensors die. LP-BTS is a learning-guided planning architecture: a graph proposal policy concentrates a small candidate support, a learned value critic evaluates leaves, and edge-budgeted PUCT compares short simulated futures before committing an action. Because the policy scores this set without a fixed output head, a single frozen checkpoint covers every evaluated setting, spanning action universes from 736 to 2,813 stops. Matched ablations reveal complementary effects: uniform sampling costs 8.8 survival percentage points, while, with targeted support fixed, PUCT jointly retains 1.4 points (about 3.5 of 250 sensors) and direct policy selection travels 23% farther. On a prospectively specified, sealed 30-scenario confirmatory bank evaluated once, LP-BTS attains the highest observed survival (0.4545) and alive-AUC (0.8031). Its estimated survival advantage over the strongest domain-engineered comparator is +0.0066 (95% CI [-0.0037, +0.0184]), an unresolved difference, while it exceeds a deadline heuristic and two source-derived direct-policy reconstructions on every paired scenario. Both learned rows are trained, source-derived reconstructions of variants reported by Gong et al. In this setting, the results provide controlled evidence about learning-guided planning in a large, dynamic action space.

Tue 15 SeptMachine LearningArtificial Intelligence
The gist
Planning which sensors to charge is hard when there are many choices that change over time. The authors show a new method that uses a smart guess to narrow down options and then carefully picks the best choices by simulating short futures. Their approach works well with hundreds of sensors and adapts as sensors run out of power. Tests show it performs better than simple or fixed methods and matches or beats other advanced approaches.
Open 2609.17429v1

Multirotor control limits found in propeller speed reversals and redundancies

Differential Realizability of Static Control Allocation in Multirotors: An Impossibility under Nonredundant Full Actuation and a Pseudoinverse Obstruction under Redundant Actuation

Abstract: Control allocation for multirotors with bidirectional propellers is commonly formulated in signed-thrust variables, where the wrench map is linear. The signed-quadratic map from physical rotor speed to thrust, however, is not a local diffeomorphism at zero speed. This work derives two distinct consequences. Under nonredundant full actuation, a single-propeller reversal removes one instantaneous task direction; hence, no global continuously differentiable exact static allocator exists over the complete task space. Under redundant actuation, the physical task map may remain regular, yet a transverse pseudoinverse zero crossing requires an unbounded rotor-speed derivative. We define differential realizability as regularity of the physical lift of an actuator-output section, derive exact and first-order validity conditions, and distinguish structural rank loss from an allocator- induced rate singularity. A local nullspace deformation repairs isolated pseudoinverse reversals, while a global fixed-orthant construction establishes existence of regular sections at the cost of persistent task-preserving internal actuation.

Fri 11 SeptRobotics
The gist
Controlling multirotor drones involves adjusting propeller speeds to move as desired, but the relationship between propeller speed and generated force is tricky near zero speed. The authors found that when propellers are not redundant, changing a single propeller’s direction can cause a loss in the drone's ability to move instantly in some directions. With extra (redundant) propellers, the system can stay regular but controlling it smoothly requires difficult adjustments that can become infinitely fast. They also provide ways to fix or work around these control issues through mathematical methods.
Open 2609.13588v1