Relational Reasoning for Multi-Agent Systems
Abstract: Multi-agent interacting systems are prevalent in the world, from purely physical systems to complicated social dynamics systems. The interactions between entities / components can give rise to very complex behavior patterns at the level of both individuals and the whole system. In many real-world multi-agent interacting systems (e.g., traffic participants, mobile robots, sports players), [...]
Carnegie Mellon University
Dynamical Model Learning and Inversion for Aggressive Quadrotor Flight
Quadrotor applications have seen a surge recently and many tasks require precise and accurate controls. Flying fast is critical in many applications and the limited onboard power source makes completing tasks quickly even more important. Staying on a desired course while traveling at high speeds and high accelerations is difficult due to complex and stochastic [...]
MonoClothCap: Towards Temporally Coherent Clothing Capture from Monocular RGB Video
Abstract: We present a method to capture temporally coherent dynamic clothing deformation from a monocular RGB video input. In contrast to the existing literature, our method does not require a pre-scanned personalized mesh template, and thus can be applied to in-the-wild videos. To constrain the output to a valid deformation space, we build statistical deformation [...]
Towards an Intelligence Architecture for Human-Robot Teaming
Abstract: Advances in autonomy are enabling intelligent robotic systems to enter human-centric environments like factories, homes and workplaces. To be effective as a teammate, we expect robots to accomplish more than performing simplistic repetitive tasks; they must perceive, reason, perform semantic tasks in a human-like way. A robot's ability to act intelligently is fundamentally tied [...]
Carnegie Mellon University
Robust Manipulation with Active Compliance
Abstract: Human manipulation skills are filled with creative use of physical contacts to move the object about the hand and in the environment. However, it is difficult for robot manipulators to enjoy this dexterity since contacts may cause the manipulation task to fail by introducing huge forces or unexpected change of constraints, especially when modeling [...]
Carnegie Mellon University
Open-world Object Detection and Tracking
Abstract: Computer vision today excels at recognizing narrow slices of the real world: our models seem to accurately detect objects like cats, cars, or chairs in benchmark datasets. However, deploying models requires that they work in the open world, which includes arbitrary objects in diverse settings. Current methods struggle on both axes: they recognize only [...]
Self-supervised learning for visual recognition
Abstract: We are interested in learning visual representations that are discriminative for semantic image understanding tasks such as object classification, detection, and segmentation in images/videos. A common approach to obtain such features is to use supervised learning. However, this requires manual annotation of images, which is costly, ambiguous, and prone to errors. In contrast, self-supervised [...]
Policy Decomposition : Approximate Optimal Control with Suboptimality Estimates
Abstract: Owing to the curse of dimensionality, numerically computing global policies to optimal control problems for complex dynamical systems quickly becomes intractable. In consequence, a number of approximation methods have been developed. However, none of the current methods can quantify by how much the resulting control underperforms the elusive globally optimal solution. We propose Policy [...]
Inverse Reinforcement Learning with Explicit Policy Estimates
Abstract: Various methods for solving the inverse reinforcement learning (IRL) problem have been developed independently in machine learning and economics. In particular, the method of Maximum Causal Entropy IRL is based on the perspective of entropy maximization, while related advances in the field of economics instead assume the existence of unobserved action shocks to explain [...]
GANs for Everyone
Abstract: The power and promise of deep generative models such as StyleGAN, CycleGAN, and GauGAN lie in their ability to synthesize endless realistic, diverse, and novel content with user controls. Unfortunately, the creation and deployment of these large-scale models demand high-performance computing platforms, large-scale annotated datasets, and sophisticated knowledge of deep learning methods. This makes [...]