🤖 Robotics & Embodied AI

🎞️ ECCV2026 · 20 paper notes

📌 Same area in other venues: 📷 CVPR2026 (146) · 🔬 ICLR2026 (162) · 💬 ACL2026 (11) · 🧪 ICML2026 (53) · 🤖 AAAI2026 (30) · 🧠 NeurIPS2025 (75)

🔥 Top topics: Robotics ×6 · Navigation ×4 · Multimodal/VLM ×4

360CityArena: A Realistic Virtual Urban Navigation Benchmark for Embodied Agents

360CityArena places 175 manually constructed tasks in an Akihabara street-network pose graph built from real panoramic videos, diagnosing seven urban navigation capabilities; the best evaluated model, Gemini-2.5 Flash, scores only 17.1% overall against 77.3% for locally familiar humans, exposing intertwined weaknesses in map-to-view alignment, sustained exploration, and fine-grained perception.

3DWay: Generalizing Robot Manipulation via 3D Consistent Waypoints

3DWay expresses 3D trajectory generation as multi-view 2D coordinate prediction in a VLM's text interface, reconstructs executable waypoints using calibrated geometry, reaches 64.0% success on unseen RLBench tasks with fixed top-down control, and improves few-shot pi0 adaptation through local spatial guidance.

AdaDexGrasp: Adaptive Dexterous Grasping via 3D Visuo-Tactile Representation Fusion

AdaDexGrasp predicts which hand part should contact each object region to generate an initial grasp, then maps measured touch back into 3D geometry to assess and correct unstable configurations, raising simulated success on unseen categories from 59% without adaptation to 83%.

AeroVLA: A Vision-Language-Action Model for UAV Navigation via Minimalist End-to-End Control

AeroVLA combines front/down views, coarse target bearings, and native numerical tokens in a reactive OpenVLA-7B policy that jointly controls UAV motion and termination, achieving 37.58% SR on unseen TravelUAV maps versus LongFly's 11.27%, while still relying on a target-bearing prior and a low-level flight controller.

AgentVLN: Towards Agentic Vision-and-Language Navigation

AgentVLN uses Qwen2.5-VL-3B to schedule perception and planning skills through geometrically grounded image prompts, with local correction and on-demand depth queries resolving navigation ambiguities, achieving 67.2% / 69.5% SR on R2R-CE / RxR-CE Val-Unseen.

AMCoNav: Asynchronous Multi-module Collaborative Framework for Embodied Visual Navigation

AMCoNav keeps a lightweight navigation policy running while large models reason on demand in the background, then uses shared context and probability fusion to choose exploration, verification, or direct navigation, achieving 60.0% / 60.0% / 53.3% success on the three HM3D-OVON splits and reducing runtime against a synchronous counterpart without guaranteeing shorter paths.

Attention-DP3: Spatially Object-aware 3D Diffusion Policy via Geometry-aligned Attentional Conditioning

Attention-DP3 projects language-specified object masks onto observed point clouds and conditions an otherwise unchanged DP3 action denoiser with three complementary semantic fields, raising MetaWorld average success from 0.669 to 0.726 and improving real-robot manipulation under clutter.

BeTTER: Diagnose the Illusion of Embodied Reasoning in Vision-Language-Action Models

BeTTER separates motor competence from semantic, compositional and state reasoning through task interventions designed to keep familiar motions executable: three representative VLAs achieve only 5%, 15% and 0% success on unseen subgoal compositions, while physical robot tests also reveal behavioral inertia and phase conflicts.

Beyond Dense Futures: World Models as Structured Planners for Robotic Manipulation

StructVLA trains a world model to predict sparse images selected around gripper transitions and motion pauses, then transfers this task-progress representation to action generation, achieving average success rates of 75.0% on SimplerEnv-WidowX and 94.8% on LIBERO.

Beyond Description: Cognitively Benchmarking Fine-Grained Action for Embodied Agents

CFG-Bench decomposes action understanding into physical interaction, temporal-causal relations, intention, and evaluation, using hybrid QA and counterfactual gating to expose the gap between description and execution knowledge; fine-grained data SFT raises Qwen2.5-VL-7B's average EB-ALFRED result from 4.7 to 9.7.

ChronoFlow Policy: Unifying Past-Future Interaction Flow in Visuomotor Policy Learning

ChronoFlow-Policy represents past, current, and future object-gripper motion as unified 3D keypoint trajectories, jointly trains future-flow recovery and action prediction, reaches average success rates of 72% on MetaWorld and 66% on RoboTwin 2.0, and improves final-stage real-world Swap-Easy success from 20% without history to 93% with history.

CoMaTrack: Competitive Multi-Agent Game-Theoretic Tracking with Vision-Language-Action Models

CoMaTrack trains two VLA robots to compete for following the same language-specified target, using asymmetric rewards and SFT-anchored GRPO as a dynamic curriculum; its 3B tracker reaches 92.1%, 74.2%, and 57.5% success on EVT-Bench STT, DT, and AT, with same-model STT success increasing from 89.5% under single-agent RL to 92.1%.

ComplexMimic: Human–Scene Interaction Imitation in Complex 3D Environments

ComplexMimic trains a strict motion-tracking expert and a collision-adaptive interaction expert, then routes supervision by motion regime and prioritizes difficult trajectories that still improve, distilling a single controller with 90.6% success on TRUMANS while retaining cross-domain pose-error trade-offs.

CoToGrasp: Contact-Topology-Conditioned Dexterous Grasp Synthesis via Canonical Workspace Learning

CoToGrasp learns requested contact topologies using robot-hand geometry alone, transfers them to unseen objects through a canonical workspace, and validates and optimizes the resulting grasps, improving topology-averaged success from 21.13% to 26.72% and topology compliance from 14.28% to 17.18% on DexGraspNet without universally outperforming unconditioned planners in physical success.

CulinaryCut: A Physics-aware Vision-Language-Action Benchmark for Food Cutting via Material Point Method

CulinaryCut couples MLS-MPM with ManiSkill to generate food-cutting demonstrations with contact-force records, exposing both ratio-grounding and physical-severing failures while improving RDT's orange-cutting success in IntSim from 23% to 59% without changing its architecture or supplying force inputs.

Deform360: A Massive Multi-view Visuotactile Dataset for Deformable World Models

Deform360 records 1,980 interactions with 198 deformable objects using 41 views and bimanual tactile sensing, builds annotations by decoupling geometry from motion, and compares video and particle world models: explicit physical priors help low-data prediction, while large-scale pretraining supports visual generalization to new objects without guaranteeing action compliance.

Depth-guided Multi-view Exposure Bracketing for HDR Robot Vision

DMEB distributes different exposures across synchronized cameras and uses external depth to align and fuse complementary observations for single-shot HDR reconstruction, achieving 39.40 dB PSNR-µ with three cameras on real modest-DR data while introducing evaluation data covering 121 real scenes and 20 synthetic videos.

DiNBV-Grasp: Real-Time Distance-Aware Two-Stage Next-Best-View for Robotic Grasping

DiNBV-Grasp separates choosing a viewpoint that resolves occlusion from choosing an observation distance that supports reliable grasping, using a distilled semantic planner for the former and a success-probability scorer for the latter; its simulation plug-in raises ManipLLM Seen/Unseen success from 56.00%/51.00% to 65.62%/58.42% without modifying the underlying manipulation model.

Domain Adaptation with Adaptive Imagination for Visual Reinforcement Learning under Limited Target Data

AIDA maps target images into the source state space, rolls out a frozen policy and dynamics model, uses a three-way discriminator to cut off each trajectory once it leaves the target manifold, and applies state→image→state consistency to the retained prefix; with only 50 target trajectories, it obtains the best return ratio on 6 of 7 tasks and the lowest state RMSE on all seven.

GEM: Generative Supervision Helps Embodied Intelligence

GEM adds conditional depth generation to embodied vision-language model training and progressively aligns its components to encode geometry in shared representations, raising the 8B model's VSI-Bench score from 68.6 with same-data SFT to 70.6, while its GEM-VLA extension reaches a 96.1% average success rate on LIBERO.