Abstract is a venture capital firm based in USA.
Round size: $64M
ABSTRACT Chemical pollutants released into the environment are transported by turbulent flows, generating complex and intermittent plume structures that threaten ecosystems and human health. Rapid localization of emission sources is critical, and field robots equipped with chemical sensors provide a viable means to perform this task. Nevertheless, inferring source location from sensor readings remains difficult due to sparse detections and the absence of reliable concentration gradients. Existing approaches broadly fall into two paradigms. Bioinspired strategies rely on reactive b
ABSTRACT Pipelines, valves, and pressure vessels (PVs) are vital components in nuclear, thermal power, and chemical systems, operating under high temperature and pressure over extended periods, leading to safety risks. Ensuring their safe operation requires regular inspections, which current robotic systems cannot fully address due to the diverse and challenging environments. To address this need, we propose a robotic system featuring a narrow-waist spring torso, airbag foot supports, and ducted-fan-based negative-pressure adhesion. The robot exhibits three key features: First, th
ABSTRACT Autonomous navigation in complex, unstructured environments poses a significant challenge, with traditional planners lacking adaptability and end-to-end learning methods hindered by data dependency or training instability. Learning-based hybrid methods that automatically tune traditional planner parameters are a promising compromise, but we identify an underlying architectural limitation that contributes to constraining their performance: a low-frequency, high-latency control loop. This paper resolves this architectural bottleneck by proposing a multi-rate, hybrid hierarc