PixelGoal navigation specifies targets directly in the agent's camera view, providing a natural interface between high-level visual reasoning and low-level navigation. Depth can lift a visible target pixel into a metric PointGoal, but this estimate becomes unreliable under occlusion or sensor noise. Moreover, a PointGoal alone does not encode traversability or feasible paths around obstacles. We present OccPlanner, a goal-aware occupancy-conditioned diffusion planner that learns complementary egocentric goal and planning-oriented 3D representations through metric target and occupancy prediction, respectively. These representations condition a diffusion trajectory module to generate target-directed, obstacle-aware trajectories. For scalable geometric supervision, we introduce L3ROcc, which converts monocular RGB navigation videos into aligned 3D occupancy and trajectory annotations. We train OccPlanner on L3ROcc-processed InternData-N1 and evaluate it in closed-loop simulation across four unseen InternScenes categories and two goal-distance ranges. Across all eight settings, OccPlanner substantially outperforms existing open-source PixelGoal approaches and achieves competitive performance against PointGoal planners with direct metric-goal inputs.
Binling Huang, Nianjin Ye, Xi Yang et al.· 0 citations
End-to-end autonomous driving has increasingly adopted world model-based reinforcement learning frameworks to improve learning efficiency through \textit{imagined rollouts}. However, existing world models suffer from three key limitations: temporal inconsistency in long-horizon imagined rollouts, inadequate modeling of ego-environment interactions, and limited adaptability to diverse driving styles. To address these challenges, we propose \textit{StyleDrive}, a world-model-based learning framework that jointly enforces long-horizon consistency, explicitly disentangles interactive traffic states, and supports multi-style policy optimization within a unified learning paradigm. First, we introduce a temporal consistency regularization that integrates historical latent states through gated cross-attention, stabilizing long-horizon imagined rollouts and mitigating error accumulation. Second, we design an explicit state disentanglement module that separates ego-relevant from ego-irrelevant interactive states, enabling more interpretable and efficient decision-making in complex traffic scenarios. Third, we enable multi-style driving behaviors through Group Relative Policy Optimization, which replaces per-step reward optimization with trajectory-wise relative advantages, reducing reward variance and supporting diverse driving styles without retraining. We evaluate StyleDrive on the Bench2Drive closed-loop driving benchmark, achieving a driving score of 88.44 (+17.08 over the previous best world model-based method) and a success rate of 66.82 (+16.58). Furthermore, we deploy StyleDrive on a real automated guided vehicle platform and demonstrate promising sim-to-real transfer capability in dynamic driving scenarios.
Yuxuan Han, Kun-Yuan Wu, Liyunong Yang et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
