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Understanding and learning the actor-to-X interactions (AXIs), such as those between the focal vehicles (actor) and other traffic participants, such as other vehicles and pedestrians, as well as traffic environments like the city or road map, is essential ...

Reproducing realistic collective behaviors presents a captivating yet formidable challenge. Traditional rule-based methods rely on hand-crafted principles, limiting motion diversity and realism in generated collective behaviors. Recent imitation learning ...

Humanoid Reaction Synthesis is pivotal for creating highly interactive and empathetic robots that can seamlessly integrate into human environments, enhancing the way we live, work, and communicate. However, it is difficult to learn the diverse ...

We address the real problem of safe, robust, adaptive resource oversubscription in uncertain environments with our proposed novel technique of chance-constrained imitation learning. Our objective is to enhance resource efficiency while ensuring safety ...

Humans can leverage physical interaction to teach robot arms. This physical interaction takes multiple forms depending on the task, the user, and what the robot has learned so far. State-of-the-art approaches focus on learning from a single modality, or ...

Achieving carbon neutrality within industrial operations has become increasingly imperative for sustainable development. It is both a significant challenge and a key opportunity for operational optimization in industry 4.0. In recent years, Deep ...

This research presents a novel deep learning framework designed to enable autonomous navigation of unmanned aerial vehicle (UAV) swarms through confined spaces, specifically focusing on the challenge of hole crossing. Leveraging the Gazebo simulation ...

Assistive robot arms try to help their users perform everyday tasks. One way robots can provide this assistance is shared autonomy. Within shared autonomy, both the human and robot maintain control over the robot’s motion: as the robot becomes confident ...

We develop a framework to learn bio-inspired foraging policies using human data. We conduct an experiment where humans are virtually immersed in an open field foraging environment and are trained to collect the highest amount of rewards. A Markov ...

Congestion control has been a fundamental research focus in web transmission for over 30 years. However, with diverse network scenarios like cellular networks and WiFi, traditional models might no longer accurately describe current network conditions -- ...

Imitation learning field requires expert data to train agents in a task. Most often, this learning approach suffers from the absence of available data, which results in techniques being tested on its dataset. Creating datasets is a cumbersome process ...

Deep generative replay has emerged as a promising approach for continual learning in decision-making tasks. This approach addresses the problem of catastrophic forgetting by leveraging the generation of trajectories from previously encountered tasks to ...

Large-scale multi-agent pathfinding (MAPF) presents significant challenges in several areas. As systems grow in complexity with a multitude of autonomous agents operating simultaneously, efficient and collision-free coordination becomes paramount. ...

Offline learning derives effective policies from expert demonstrators' datasets without direct interaction. While recent research consider dataset characteristics like expertise level or multiple demonstrators, a distinct approach is necessary in zero-...

Policy-guided offline reinforcement learning (POR) decomposes the offline reinforcement learning (offline RL) problem into goal estimation and goal-conditioned execution subproblems, leading to improved performance. However, we reveal that the ...

Imitation Learning (IL) algorithms show promise in learning human-level driving behavior, but they often suffer from "causal confusion," a phenomenon where the lack of explicit inference of the underlying causal structure can result in misattribution of ...

Nonspeaking autistic individuals ("nonspeakers") represent about one-third of the autistic population, and most are never provided with an effective alternative to speech, hindering their educational, employment, and social opportunities. Some ...

To act in the world, robots rely on a representation of salient task aspects: for example, to carry a coffee mug, a robot may consider movement efficiency or mug orientation in its behavior. However, if we want robots to act for and with people, their ...

Imitation learning, which learns agent policy by mimicking expert demonstration, has shown promising results in many applications such as medical treatment regimes and self-driving vehicles. However, it remains a difficult task to interpret control ...

The diverse advertiser demands (brand effects or immediate outcomes) lead to distinct selling (pre-agreed volumes with an under-delivery penalty or compete per auction) and pricing (fixed prices or varying bids) patterns in Guaranteed delivery (GD) and ...