Generative Retrieval (GR) offers a promising paradigm for recommendation through next-token prediction (NTP). However, scaling it to large-scale industrial systems introduces three challenges: (i) within a single request, the identical model inputs may produce inconsistent outputs due to the pagination request mechanism; (ii) the prohibitive cost of encoding long user behavior sequences with multi-token item representations based on semantic IDs, and (iii) aligning the generative policy with nuanced user preference signals. We present GenRec, a preference-oriented generative framework deployed on the JD App https://www.jd.com that addresses above challenges within a single decoder-only architecture. For training objective, we propose Page-wise NTP task, which supervises over an entire interaction page rather than each interacted item individually, providing denser gradient signal and resolving the one-to-many ambiguity of point-wise training. On the prefilling side, an asymmetric linear Token Merger compresses multi-token Semantic IDs in the prompt while preserving full-resolution decoding, reducing input length by ~2× with negligible accuracy loss. To further align outputs with user satisfaction, we introduce GRPO-SR, a reinforcement learning method that pairs Group Relative Policy Optimization with NLL regularization for training stability, and employs Hybrid Rewards combining a dense reward model with a relevance gate to mitigate reward hacking. In month-long online A/B tests serving production traffic, GenRec achieves 9.5% improvement in click count and 8.7% in transaction count over the existing pipeline.

Object Goal Navigation (ObjectNav) task is to navigate an agent to an object category in unseen environments without a pre-built map. In this paper, we solve this task by predicting the distance to the target using semantically-related objects as cues. Based on the estimated distance to the target object, our method directly choose optimal midterm goals that are more likely to have a shorter path to the target. Specifically, based on the learned knowledge, our model takes a bird's-eye view semantic map as input, and estimates the path length from the frontier map cells to the target object. With the estimated distance map, the agent could simultaneously explore the environment and navigate to the target objects based on a simple human-designed strategy. Empirical results in visually realistic simulation environments show that the proposed method outperforms a wide range of baselines on success rate and efficiency. Real-robot experiment also demonstrates that our method generalizes well to the real world.

Reliable robotic grasping in unstructured environments is a crucial but challenging task. The main problem is to generate the optimal grasp of novel objects from partial noisy observations. This paper presents an end-to-end grasp detection network taking one single-view point cloud as input to tackle the problem. Our network includes three stages: Score Network (SN), Grasp Region Network (GRN), and Refine Network (RN). Specifically, SN regresses point grasp confidence and selects positive points with high confidence. Then GRN conducts grasp proposal prediction on the selected positive points. RN generates more accurate grasps by refining proposals predicted by GRN. To further improve the performance, we propose a grasp anchor mechanism, in which grasp anchors with assigned gripper orientations are introduced to generate grasp proposals. Experiments demonstrate that REGNet achieves a success rate of 79.34% and a completion rate of 96% in real-world clutter, which significantly outperforms several state-of-the-art point-cloud based methods, including GPD, PointNetGPD, and S4G. The code is available at https://github.com/zhaobinglei/REGNet for 3D Grasping.