Papers for

robotics deployment teams

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.

Language guided robots adapt instantly with one phone scan

One Scan is Enough: Demonstration-Free Adaptation for Language-Guided Navigation

Abstract: Deploying a language-guided navigation policy in a new building usually means watching it fail. Public-benchmark VLAs degrade scene by scene as layout, appearance, and language drift, and the standard fix of teleoperating fresh trajectories on site does not scale. We replace human demonstration with a single brief iPhone scan. Scan2Instruction compiles that scan into a complete training environment, automatically synthesizing executable navigation episodes with grounded instructions. PRPT-KS then adapts the policy through a closed-form Pareto utility read directly off the reconstructed scene, with no learned critic and no human labels. Across five real environments, the two designs together drive our RealNav to an 85% real-robot success rate, compared with 49% for a RealNav baseline post-trained on human demonstrations, and still achieve 82% in unscanned regions of the same buildings, compared with 41% for the same baseline. Our work turns demonstration-free scene adaptation for language-guided navigation from an aspiration into a working reality.

Sat 24 OctRoboticsArtificial IntelligenceComputation and Language
The gist
Robots that navigate indoors often struggle when placed in new buildings because the layouts and appearances differ from what they learned before. The authors introduce a method that allows these robots to quickly adapt by scanning the new environment once using a phone, without needing detailed human demonstrations. This single scan creates a virtual map and instructions automatically, letting the robot retrain itself more effectively. The new approach significantly improves success rates in real buildings compared to older methods relying on human input.
Open → 2610.33794v1

Legged robots adapt quickly to different shoes for better walking

SHOES: Sim-to-real Hardware-adaptation via On-policy Expansion of Skills for Legged Robots

Abstract: Humans expand their locomotion capability by wearing appropriate footwear, such as hiking boots for mountains or skates for ice. Footwear changes the ground-contact interface and enables traversal of otherwise difficult terrain. In contrast, locomotion controllers are typically trained for a fixed morphology, and their performance can degrade when shoes alter foot geometry, mass, and friction. We propose SHOES, a sample-efficient framework for adapting locomotion policies to footwear-induced morphology changes. SHOES freezes a simulation-trained nominal policy and fine-tunes only lightweight, identity-initialized modulation modules on the robot, preserving the initial behavior while enabling efficient hardware adaptation. We also introduce SafeBox, a workspace-aware waypoint sampler that constrains online adaptation within a user-defined workspace. The simulation results with a shoe-equipped Unitree Go2 show that SHOES substantially recovers locomotion performance after footwear-induced morphology changes using limited additional adaptation. Under the same adaptation budget, SHOES outperforms FiLM-only, Linear Adapter-only, and residual-RL baselines. Real-world experiments on a Unitree Go2 fitted with human shoes further evaluate the effectiveness of SHOES for hardware deployment.

Mon 12 OctRobotics
The gist
Robots that walk can struggle when their feet change, like when you wear new shoes. The authors designed a method called SHOES that helps robots quickly adjust to these changes without relearning everything from scratch. Their approach tweaks small parts of the robot’s walking control while keeping the original walking style mostly the same. They tested this on real robots wearing human shoes and found it works better than other methods.
Open → 2610.16457v1