Forecast-aware Gaussian Splatting for Predictive 3D Representation in Language-Guided Pick-and-Place Manipulation

• URL: http://arxiv.org/abs/2605.11144v1
• Date: Mon, 11 May 2026 18:48:26 GMT
• Title: Forecast-aware Gaussian Splatting for Predictive 3D Representation in Language-Guided Pick-and-Place Manipulation
• Abstract summary: We introduce Forecast-aware Gaussian Splatting (Forecast-GS), a predictive 3D representation framework for language-conditioned robotic manipulation.<n>We validate Forecast-GS on real-world pick-and-place manipulation tasks, including Cutter-to-Box, Apple-to-Bowl, and Sponge-to-Tray.<n>Forecast-GS with automatic candidate selection achieves success rates of 21/25, 23/25, and 16/25 on the three tasks, respectively.
• Score: 4.711302998453154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Forecast-aware Gaussian Splatting (Forecast-GS), a predictive 3D representation framework for language-conditioned robotic manipulation. While recent manipulation systems have made progress by grounding language instructions into robot affordances, value maps, or relational keypoint constraints, they usually reason over the current scene and do not explicitly model the task-completed state. This limitation is critical when success depends on satisfying spatial and semantic goals under partial observations, where the robot must evaluate whether a candidate action leads to a feasible task-consistent outcome. We validate Forecast-GS on real-world pick-and-place manipulation tasks, including Cutter-to-Box, Apple-to-Bowl, and Sponge-to-Tray. For each task, we conduct 25 real-world trials under varied initial object configurations using the same robot platform and sensing setup. Forecast-GS with automatic candidate selection achieves success rates of 21/25, 23/25, and 16/25 on the three tasks, respectively, outperforming the ReKep baseline, which achieves 15/25, 19/25, and 10/25. A diagnostic human-assisted setting further improves success rates to 23/25, 24/25, and 19/25, suggesting that candidate generation is effective while automatic ranking remains imperfect. These results suggest that explicitly forecasting task-completed 3D states enables more reliable action evaluation, while the gap between automatic and human-assisted selection indicates that robust final-state ranking remains an important challenge for fully autonomous manipulation. Overall, Forecast-GS provides an interpretable bridge between language understanding, 3D perception, and robotic manipulation planning.

Related papers

• Dynin-Robotics: Omnimodal Unified Diffusion Vision-Language-Action Model [7.384043308834925]
  Visual goal and dynamics prediction can provide language-conditioned robot policies with both a target outcome and a representation of action-dependent scene changes.<n>Dynin-Robotics implements this formulation on Dynin- Omni, representing language, visual observations, goals, and actions as discrete tokens.<n>We continually pretrain the model on approximately 1.33 million trajectories from 48 Open X-Embodiment datasets and adapt it separately to downstream domains.
  arXiv  Detail & Related papers  (2026-09-11T16:49:26Z)
• GUIDER: Evaluating Goal-Free Human Intent Inference for Teleoperated Manipulation on Real-Robot Data [1.7107261936393963]
  We deploy the Global User Intent Dual-phase Estimation for Robots to test the manipulation phase across various assistance scenarios.<n>We add online probability updates, workspace limits, support-plane filtering, and a grasping mode that prioritizes feasible grasp regions.<n>Across 20 manipulation steps in three scenarios, GUIDER estimated human intent within the correct grasp-candidate set in all cases.
  arXiv  Detail & Related papers  (2026-08-15T23:16:24Z)
• AFUN: Towards an Affordance Foundation Model for Functionality Understanding [12.890216832485647]
  We present ourmodel, a step towards an affordance foundation model for functionality understanding.<n>We build a large-scale standardized data pipeline that converts heterogeneous robot, human, simulation, and real-world scan data into a shared affordance schema.<n>Ourmodel outperforms all baselines by a large margin across 8 test sets from 4 benchmarks.
  arXiv  Detail & Related papers  (2026-06-01T17:50:16Z)
• Rethinking Visual-Language-Action Model Scaling: Alignment, Mixture, and Regularization [65.37179698521766]
  Vision-Language-Action (VLA) models show strong promise for generalist robot control.<n>It remains unclear whether -- and under what conditions -- the standard "scale data" recipe translates to robotics.<n>We present a systematic, controlled study of VLA scaling that revisits core training choices for pretraining across diverse robots.
  arXiv  Detail & Related papers  (2026-02-10T12:25:43Z)
• UniFField: A Generalizable Unified Neural Feature Field for Visual, Semantic, and Spatial Uncertainties in Any Scene [11.224584333257338]
  We present UniFField, a unified uncertainty-aware neural feature field that combines visual, semantic, and geometric features in a single generalizable representation.<n>We evaluate our uncertainty estimations to accurately describe the model prediction errors in scene reconstruction and semantic feature prediction.
  arXiv  Detail & Related papers  (2025-10-08T08:30:26Z)
• Probabilistic Human Intent Prediction for Mobile Manipulation: An Evaluation with Human-Inspired Constraints [2.2893865000399938]
  Accurate inference of human intent enables human-robot collaboration without constraining human control or causing conflicts between humans and robots.<n>We present GUIDER, a probabilistic framework that enables a robot to estimate the intent of human operators.<n>We evaluated GUIDER on 25 trials (five participants x five task variants) in Isaac Sim, and compared it with two baselines, one for navigation and one for manipulation.
  arXiv  Detail & Related papers  (2025-07-14T10:21:27Z)
• AnchorDP3: 3D Affordance Guided Sparse Diffusion Policy for Robotic Manipulation [8.603450327406879]
  AnchorDP3 is a diffusion policy framework for dual-arm robotic manipulation.<n>It is trained on large-scale, procedurally generated simulation data.<n>It achieves a 98.7% average success rate in the RoboTwin benchmark.
  arXiv  Detail & Related papers  (2025-06-24T03:03:26Z)
• DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving [62.62464518137153]
  DriveTransformer is a simplified E2E-AD framework for the ease of scaling up.<n>It is composed of three unified operations: task self-attention, sensor cross-attention, temporal cross-attention.<n>It achieves state-of-the-art performance in both simulated closed-loop benchmark Bench2Drive and real world open-loop benchmark nuScenes with high FPS.
  arXiv  Detail & Related papers  (2025-03-07T11:41:18Z)
• SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation [62.58480650443393]
  Segment Anything (SAM) is a vision-foundation model for generalizable scene understanding and sequence imitation. We develop a novel multi-channel heatmap that enables the prediction of the action sequence in a single pass.
  arXiv  Detail & Related papers  (2024-05-30T00:32:51Z)
• RH20T-P: A Primitive-Level Robotic Dataset Towards Composable Generalization Agents [105.13169239919272]
  We propose RH20T-P, a primitive-level robotic manipulation dataset.<n>It contains about 38k video clips covering 67 diverse manipulation tasks in real-world scenarios.<n>We standardize a plan-execute CGA paradigm and implement an exemplar baseline called RA-P on our RH20T-P.
  arXiv  Detail & Related papers  (2024-03-28T17:42:54Z)
• A Control-Centric Benchmark for Video Prediction [69.22614362800692]
  We propose a benchmark for action-conditioned video prediction in the form of a control benchmark. Our benchmark includes simulated environments with 11 task categories and 310 task instance definitions. We then leverage our benchmark to study the effects of scaling model size, quantity of training data, and model ensembling.
  arXiv  Detail & Related papers  (2023-04-26T17:59:45Z)
• Spatial-Language Attention Policies for Efficient Robot Learning [32.215861214516735]
  We propose Spatial-Language Attention Policies (SLAP) as a solution. SLAP uses three-dimensional tokens as the input representation to train a single multi-task, language-conditioned action prediction policy. We show an 80% success rate in the real world across eight tasks with a single model, and a 47.5% success rate when unseen clutter and unseen object configurations are introduced.
  arXiv  Detail & Related papers  (2023-04-21T20:02:49Z)
• Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation? [54.442692221567796]
  Task specification is critical for engagement of non-expert end-users and adoption of personalized robots. A widely studied approach to task specification is through goals, using either compact state vectors or goal images from the same robot scene. In this work, we explore alternate and more general forms of goal specification that are expected to be easier for humans to specify and use.
  arXiv  Detail & Related papers  (2022-04-23T19:39:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.