Bridging the sim2real gap in the table tennis robot with a transformer-based ball states predictor

• URL: http://arxiv.org/abs/2606.11464v1
• Date: Tue, 09 Jun 2026 21:35:59 GMT
• Title: Bridging the sim2real gap in the table tennis robot with a transformer-based ball states predictor
• Abstract summary: Robotic table tennis is a representative benchmark for high-speed, closed-loop robotic control in dynamic environments.<n>We propose a transformer-based framework for table tennis ball state prediction.<n>We collect a large-scale real-world dataset from players of varying skill levels and diverse ball cannon configurations.
• Score: 42.252953556229066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robotic table tennis is a representative benchmark for high-speed, closed-loop robotic control in dynamic environments, where accurate and fast prediction of ball states is critical for reliable planning and control. Physics-based approaches rely heavily on accurate parameter identification and precise initial state, while learning-based methods often struggle to capture long-range temporal dependencies and are typically trained on limited or simulated data. We propose a transformer-based framework for table tennis ball state prediction that leverages attention mechanisms to model long-range temporal correlations directly from historical observations, without relying on explicit flight or bounce models. To support robust learning and generalization, we collected a large-scale real-world dataset from players of varying skill levels and diverse ball cannon configurations. The combination of a high-capacity transformer architecture and extensive real-world data enables accurate long-horizon forecasting. Building on this capability, we introduce a plug-and-play sim-to-real transfer strategy, Swap Predictor at Deployment (SPAD), which replaces the physics-based simulator used during training with the proposed real-world-trained predictor at deployment, improving the sim-to-real transferability of the policy without requiring retraining. We demonstrate that this simple substitution effectively narrows the sim-to-real gap while preserving the efficiency and scalability of simulation-based training.

Related papers

• Enhancing Sim2Real Transfer for Torque-Controlled Robots through Real2Sim Dynamics Estimation and Reinforcement Learning [2.071516130824992]
  Transferring reinforcement learning policies from simulation to Real-World robots remains a major challenge.<n>We propose a Real2Sim2Real pipeline that improves Sim2Real transfer for torque-controlled robotic arms.<n>This work highlights the effectiveness of accurate physical modelling in enabling stable and generalizable torque-based reinforcement learning policies.
  arXiv  Detail & Related papers  (2026-08-23T22:25:33Z)
• ComSim: Building Scalable Real-World Robot Data Generation via Compositional Simulation [90.4702774169675]
  We propose a novel hybrid approach called Compositional Simulation, which combines classical simulation and neural simulation.<n>Our approach utilizes a closed-loop real-sim-real data augmentation pipeline, leveraging a small amount of real-world data.<n>We train a neural simulator to transform classical simulation videos into real-world representations, improving the accuracy of policy models trained in real-world environments.
  arXiv  Detail & Related papers  (2026-04-13T12:25:45Z)
• Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation [46.80452332618188]
  Simulation Distillation (SimDist) is a framework that distills structural priors from a simulator into a latent world model.<n>By transferring reward and value models directly from simulation, SimDist provides dense planning signals from raw perception.<n>SimDist substantially outperforms prior methods in data efficiency, stability, and final performance.
  arXiv  Detail & Related papers  (2026-03-16T18:00:23Z)
• D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping [66.22412592525369]
  We introduce a real-to-sim-to-real engine that leverages the Gaussian Splat representations to build a differentiable engine.<n>We show that our engine achieves accurate and robust performance in mass identification across various object geometries and mass values.<n>Those optimized mass values facilitate force-aware policy learning, achieving superior and high performance in object grasping.
  arXiv  Detail & Related papers  (2026-03-01T15:32:04Z)
• Contact-Aware Neural Dynamics [42.83872574010789]
  We propose an implicit sim-to-real framework that learns to align the simulator's dynamics with contact information.<n>We show that using tactile contact information from robotic hands can effectively model the non-smooth discontinuities inherent in contact-rich tasks.
  arXiv  Detail & Related papers  (2026-01-19T07:56:13Z)
• Data-Driven Dynamic Parameter Learning of manipulator robots [0.8679862302950613]
  We propose a Transformer-based approach for dynamic parameter estimation.<n>The dataset consists of 8,192 robots with varied inertial and frictional properties.<n>Our model effectively captures both temporal and spatial dependencies.
  arXiv  Detail & Related papers  (2025-12-09T16:15:58Z)
• Learning Contact Dynamics for Control with Action-conditioned Face Interaction Graph Networks [4.942750934906048]
  We present a learnable physics simulator that provides accurate motion and force-torque prediction of robot end effectors in contact-rich manipulation.<n>The proposed model extends the state-of-the-art GNN-based simulator with novel node and edge types.
  arXiv  Detail & Related papers  (2025-09-15T17:15:31Z)
• Autonomous Vehicle Controllers From End-to-End Differentiable Simulation [57.278726604424556]
  We propose a differentiable simulator and design an analytic policy gradients (APG) approach to training AV controllers.<n>Our proposed framework brings the differentiable simulator into an end-to-end training loop, where gradients of environment dynamics serve as a useful prior to help the agent learn a more grounded policy.<n>We find significant improvements in performance and robustness to noise in the dynamics, as well as overall more intuitive human-like handling.
  arXiv  Detail & Related papers  (2024-09-12T11:50:06Z)
• Bridging the Sim-to-Real Gap with Bayesian Inference [53.61496586090384]
  We present SIM-FSVGD for learning robot dynamics from data. We use low-fidelity physical priors to regularize the training of neural network models. We demonstrate the effectiveness of SIM-FSVGD in bridging the sim-to-real gap on a high-performance RC racecar system.
  arXiv  Detail & Related papers  (2024-03-25T11:29:32Z)
• Residual Physics Learning and System Identification for Sim-to-real Transfer of Policies on Buoyancy Assisted Legged Robots [14.760426243769308]
  In this work, we demonstrate robust sim-to-real transfer of control policies on the BALLU robots via system identification. Rather than relying on standard supervised learning formulations, we utilize deep reinforcement learning to train an external force policy. We analyze the improved simulation fidelity by comparing the simulation trajectories against the real-world ones.
  arXiv  Detail & Related papers  (2023-03-16T18:49:05Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)

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.