Toyota Develops 'Grand Behavior Model' for Robots, Reducing Data Requirements by 80%

The Toyota Research Institute (TRI) has recently published groundbreaking research on Large Behavior Models (LBMs) that could revolutionize how robots learn and perform tasks. According to the study, LBMs can reduce the data required for learning new tasks by up to 80%, and a single model can master hundreds of different operational skills. The research paper, titled "A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation," is available on arXiv. One of the authors, Russ Tedrake—vice president of TRI and a professor at MIT—posted on social media, “LBMs really work! As the volume of pre-training data increases, we see consistent and statistically significant improvements.” Traditional robot training methods are often limited and inefficient. Each task typically requires individual programming, leading to slow and inconsistent learning processes that are confined to narrowly defined tasks and highly controlled environments. In contrast, LBMs adopt an architecture similar to Large Language Models (LLMs), but they are optimized for physical robotic operations. The LBM architecture used in this TRI study is a complex neural network based on diffusion models and Transformers. It integrates visual information from multiple cameras (including cameras mounted on the robot’s wrists and static scene cameras), proprioceptive data from the robot's own sensors, and natural language instructions from humans. This multi-modal system learns to output a sequence of coherent and precise actions directly. Specifically, these models predict action sequences for up to 16 time steps (approximately 1.6 seconds) ahead, ensuring smooth and anticipatory operations. The hardware platform used in the experiments features a dual-arm system based on the Franka Panda FR3 manipulator, equipped with up to six cameras. On the perception side, the model uses a pre-trained CLIP visual transformer to extract image features and a CLIP text encoder to process task descriptions. These visual and language features are combined with proprioceptive data and diffusion time step encoding to form the observation feature. For action generation, the LBMs employ Denoising Diffusion Implicit Models (DDIMs) to produce continuous robot actions through an iterative denoising process. To validate the effectiveness of LBMs, the research team trained multiple LBMs on nearly 1,700 hours of robot demonstration data, including 468 hours of internally collected teleoperation data, 45 hours of simulation-collected teleoperation data, 32 hours of Universal Manipulation Interface (UMI) data, and about 1,150 hours of curated internet data from the Open X-Embodiment dataset. Real-world and simulation evaluations were conducted with 1,800 real-world trials and over 47,000 simulation trials across 29 different tasks. To ensure reliability, the team used blind A/B testing and developed a new statistical evaluation framework. The study revealed three key findings: 1. Fine-tuned LBMs consistently outperformed single-task baseline models on seen tasks. In both nominal conditions and distribution shifts, the fine-tuned LBM showed statistically significant advantages in both real-world and simulation environments. 2. LBMs demonstrated greater robustness. While overall task performance decreased under distribution shifts, fine-tuned LBMs showed better adaptability compared to strategies trained from scratch. In simulation, the proportion of tasks where fine-tuned LBMs outperformed single-task strategies increased from 3/16 under nominal conditions to 10/16 under distribution shifts. 3. Perhaps most importantly, LBMs significantly reduced the data needed to learn new tasks. To achieve similar performance levels in simulation, fine-tuning an LBM required less than 30% of the data needed for training from scratch. In real-world tasks, this advantage was even more pronounced—a mere 15% of the data was sufficient for the LBM to surpass the performance of single-task baseline models trained on full datasets. The research also explored the scaling law for LBMs. By using different amounts of pre-training data, the team found that model performance improved steadily as the data volume increased. Even at the current scale, no performance discontinuities or sharp breakpoints were observed, indicating that the benefits of artificial intelligence scaling extend to the field of robotics. To test the limits of LBMs, the team designed complex, long-term tasks. One such task involved the robot using an apple corer to remove the core of an apple, retrieving a knife from a rack, unsheathing it, slicing the apple in half, further cutting it into slices, and finally cleaning the knife and re-sheathing it before returning it to the rack. LBMs performed exceptionally well on these challenging tasks, surpassing traditional methods. An important contribution of this research is its emphasis on statistical rigor in evaluating robot learning. The team pointed out that many robotics papers may measure statistical noise rather than actual effects due to insufficient statistical power. They demonstrated the impact of trial sizes on confidence intervals—for example, with 50 trials, the confidence interval width is typically 20-30% of the absolute success rate, making it difficult to reliably measure anything but the largest effects. To address this, the researchers employed Bayesian analysis methods, calculating the posterior distribution of success rates with uniform Beta priors and indicating statistical significance using Compact Letter Display (CLD). The results suggest that even with relatively small data sets, pre-training can lead to consistent performance gains, creating a positive feedback loop between data acquisition and performance improvement. As more tasks are included in the pre-training data mix, the overall performance of LBMs is expected to continue improving steadily. However, the study also identified some limitations. Unfine-tuned pre-trained LBMs exhibited inconsistent performance, partly due to limitations in their language-guided capabilities. The team found that larger visual-language behavior prototypes showed promise in internal tests, but further work is needed to verify these results. Additionally, seemingly minor design choices, such as data standardization, had significant impacts on downstream performance, often surpassing the effects of architectural or algorithmic improvements. This highlights the importance of isolating these design choices to avoid confusion about the sources of performance changes. This research represents a significant step forward in making robot learning more efficient and robust, potentially transforming how robots are trained and deployed in various settings.