arXiv subjects

Andrew Jaeyong Choi

Publications and source records attributed to Andrew Jaeyong Choi.

8 recordsLinked to original sources

arXiv2026

From Legs to Wheels: Embodiment-Aware Human Motion Retargeting for Mobile-Base Humanoids

Human video offers a scalable source of robot demonstrations, yet most human-to-humanoid retargeting methods assume a legged robot with human-like kinematics. This assumption does not hold for mobile-base humanoids equipped with a wheeled base, vertical lift, and two arms. Human walking must be expressed through base motion, while torso bending may require coordinated lift and arm motion. We address this mismatch with a task-conditioned framework that assigns reconstructed human motion to base, lift, and arm responsibilities before robot-specific realization. The allocator preserves the human-derived path, stabilizes heading, separates turn and translation when needed, retimes commands to satisfy base limits, and repairs lift and arm trajectories. A deployment adapter then converts the reference to 50 Hz commands using stationary-base detection, deadband and slew-rate filtering, time-consistent playback scaling, and separate linear and angular gains. We evaluate the resulting references with human-derived task-space comparisons, policy-free simulation replay, and a qualitative execution on a physical robot.

cs.RO↗

arXiv2026

MIM-VLA: Learning Physical Interaction Representations from Gripper Motor Feedback

Vision-language-action (VLA) policies infer grasp actions primarily from visual observations and robot state, but do not explicitly represent the physical response observed after contact. We present MIM-VLA, a motor-feedback-based architecture that encodes recent gripper current, position, velocity, and signal validity as a 128-dimensional interaction token. A motor-only Motor Interaction Module (MIM) is pretrained with human-reviewed contact and interaction-phase labels and then conditions only the gripper-action pathway of SmolVLA; arm actions and the position-control interface remain unchanged. The same token supports the MEM selector VLM that compares candidate interactions and produces evidence-conditioned selections and explanations. We evaluate MIM-VLA in three real-world settings: comparing the interaction resistance of visually different objects, disambiguating visually similar real and replica objects through active probing, and gently grasping fragile objects, including held-out instances. Across 13 object pairs, MIM-VLA selects the higher-resistance object in 75.0% of trials, compared with 48.8% for the SmolVLA baseline. For the evaluated tasks, the approach uses motor feedback already available from the gripper and does not require an additional tactile array, force-torque sensor, calibrated force estimate, or direct current control.

cs.RO↗

arXiv2026

SPACE-CLIP: Spatial Perception via Adaptive CLIP Embeddings for Monocular Depth Estimation

Robotic and autonomous systems need dense spatial cues, yet adding a dedicated depth estimator can duplicate visual processing already performed by a multimodal model. CLIP-based depth methods offer an alternative, but commonly rely on text-derived conditioning or backbone adaptation. We present SPACE-CLIP, a decoder-only framework for supervised monocular depth estimation with a frozen CLIP vision backbone and no text encoder at inference. A FiLM-conditioned semantic pathway combines global image context with multilevel patch features, while a structural pathway supplies separately processed spatial features to a hierarchical fusion decoder. Indoor and outdoor evaluations demonstrate depth reconstruction with this architecture, and controlled component comparisons support the contribution of the structural pathway. Layer-selection experiments and frequency interventions further characterize the structural pathway's contribution to depth reconstruction. A shared-backbone microbenchmark further illustrates the reduction in duplicated computation. SPACE-CLIP provides a modular approach to adding dense depth prediction to compatible visual perception stacks. Code is available at https://github.com/taewan2002/SPACE-CLIP.

cs.CV↗

arXiv2026

GeoBridge-VLA: Geometry-Aware Residual Adaptation for Vision-Language-Action Models

Vision-language-action (VLA) models encode semantic information from vision-language pretraining, but manipulation also requires precise spatial reasoning. We present GeoBridge-VLA, a two-stage method for learning geometric features from a pretrained VLA's frozen visual encoder and using them for action prediction. Stage I trains a feature bridge and geometry decoder with depth supervision. Stage II freezes these modules and trains a gated residual interface together with the action-side projections and action expert. The residual augments the existing visual tokens without adding a second image encoder or increasing the token count. Deployment requires RGB, robot state, and language, but no depth observations. Under matched evaluation conditions, GeoBridge-VLA achieves 70.9% success on LIBERO, compared with 60.0% for SmolVLA. Disabling the residual in the same trained checkpoint reduces success from 70.90% to 69.85%, with mixed effects across suites. On a physical ROBOTIS OMY robot, GeoBridge-VLA succeeds in 148 of 200 trials (74.0%) across four tasks, compared with 108 of 200 (54.0%) for SmolVLA.

cs.CV↗

arXiv2026

SPACE-CLIPv2: Decoding Local Geometry from Frozen CLIP for Monocular Depth Estimation

Vision-language foundation models such as CLIP provide strong semantic representations, but their patch tokens are not directly optimized for dense metric geometry. SPACE-CLIP showed that frozen CLIP features can support monocular depth estimation through layer-group feature fusion, yet it leaves open how neighboring CLIP tokens should be combined to recover fine local structure. We present SPACE-CLIPv2, a frozen-backbone depth decoder that aggregates fixed local neighborhoods in CLIP token space. At selected decoder stages, the model samples a fixed token stencil, predicts aggregation weights, and injects the resulting response through a gated residual update. A token-space high-pass branch further preserves shallow local contrast. On NYU Depth V2, SPACE-CLIPv2 improves over a matched SPACE-CLIP baseline, while five-seed experiments consistently favor fixed over learned-offset sampling. Zero-shot iBims-1 evaluation further improves boundary and planar-geometry measures. These results support constrained local token aggregation as a practical mechanism for decoding geometry from frozen CLIP representations.

cs.CV↗

arXiv2026

EdgeZSAD: Practical Zero-Shot Anomaly Detection on Edge Devices

Industrial inspection needs zero-shot anomaly detection (ZSAD) that remains useful under edge deployment constraints. Recent methods often rely on ViT-L foundation backbones (~300M parameters), which exceed the memory and operator budget of typical embedded hardware. We study this regime through EdgeZSAD, a compact reference system built around a TinyViT-21M-512 backbone, an asymmetric global-local readout (EdgeGLR), and a reproducible source-side training recipe (Real-IAD-DR). We train a single checkpoint in a source-trained, target-unseen protocol and evaluate it across six industrial benchmarks. Across three independent runs, the resulting model reaches an average image AUROC of 91.6 on MVTec-AD and 88.2 on VisA, while remaining directly deployable on Jetson Orin Nano Super (TensorRT FP16) and RB5 Gen2 (QNN GPU FP16). Across the six device-rescored benchmarks, image-AUROC drift stays below 0.2 points, indicating that the exported graph preserves host-side ranking behavior in the evaluated deployment setting.

cs.CV↗

arXiv2026

RetoVLA: Reusing Register Tokens for Spatial Reasoning in Vision-Language-Action Models

Vision-Language-Action (VLA) models have demonstrated robust performance across diverse robotic tasks. However, their high memory and computational demands often limit real-time deployment. While existing model compression techniques reduce the parameter footprint, they often drop in 3D spatial reasoning and scene layout understanding. This work introduces RetoVLA, an architecture designed to maintain spatial awareness in lightweight models by repurposing Register Tokens-learnable parameters originally introduced to mitigate attention artifacts in Vision Transformers. While these tokens are generally discarded once used, we repurpose them for their dense representation of global spatial context. RetoVLA integrates these recycled tokens directly into the action-planning module through a dedicated spatial context injection path. Our proposed design enables the recovery of global context without increasing the total parameter count. Real-world experiments using a 7-DOF manipulator show a 17.1%p improvement in average success rates over the baseline. Our results demonstrate that leveraging internal register tokens provides a highly effective mechanism for developing efficient, spatially-aware robotic agents. A video demonstration is available at: https://youtu.be/2CseBR-snZg

cs.RO↗

arXiv2025

NVSim: Novel View Synthesis Simulator for Large Scale Indoor Navigation

We present NVSim, a framework that automatically constructs large-scale, navigable indoor simulators from only common image sequences, overcoming the cost and scalability limitations of traditional 3D scanning. Our approach adapts 3D Gaussian Splatting to address visual artifacts on sparsely observed floors a common issue in robotic traversal data. We introduce Floor-Aware Gaussian Splatting to ensure a clean, navigable ground plane, and a novel mesh-free traversability checking algorithm that constructs a topological graph by directly analyzing rendered views. We demonstrate our system's ability to generate valid, large-scale navigation graphs from real-world data. A video demonstration is avilable at https://youtu.be/tTiIQt6nXC8

cs.RO↗