Swaayatt Robots Raises $4 Million at $151 Million Valuation
- ByStartupStory | June 10, 2024
Bhopal-based autonomous driving startup, Swaayatt Robots, has successfully raised $4 million from US-based angel investors. This funding is part of a much larger round the company is currently securing, which Founder Sanjeev Sharma announced on LinkedIn. The recently closed round values the company at $151 million, with plans to raise the remainder of the funds at an anticipated valuation of $175 million.
“Going forward, with this new fundraise of $4 million, which is part of a larger fund we are raising with global investors, we will actively be inventing new AI capabilities to solve the problem of autonomous general navigation,” Sharma stated in his LinkedIn post. The startup is focused on scaling its research and development in all areas of autonomous driving, emphasizing unsupervised and inverse reinforcement learning. The goal is to demonstrate sustainably scalable Level-5 autonomous driving technology, which requires no human intervention and lacks traditional vehicle controls like a steering wheel.
Sharma elaborated on the complexity and ambition of their mission, noting, “Autonomous agents [are] learning to negotiate the most complex, stochastic, and adversarial traffic dynamics on the roads ie to control the behaviour of a nonlinear dynamical system operating in a continuous domain, such as autonomous vehicles.” He further explained that the company has created advanced AI/mathematical models and algorithmic frameworks to handle bidirectional traffic negotiation at speeds of 40 kmph on single-lane roads, multi-agent negotiation in dynamic environments, toll-plaza and off-road navigation at high speeds, and LiDAR-less perception in both day and night conditions.
Swaayatt Robots had previously raised approximately $3 million in 2021. Speaking about the technological advancements of his startup, Sharma said, “We have better technology compared to our competitors in Europe, North America, and China…We have developed algorithmic frameworks that are more capable in terms of dealing with environmental and traffic uncertainties. At the same time, our technology is much less energy consuming, derived from the computational efficiency of our algorithmic frameworks.”
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