Swaayatt Robots (स्वायत्त रोबोट्स)

Autonomous driving and obstacles avoidance at drift speeds; challenging the limits of what is possible! In this demo, our vehicle can be seen performing #autonomousdriving at very high speeds, causing it to both skid and drift at turns, while also avoiding obstacles. At such speeds, given the inherent dynamics of the vehicle platform used, it is very easy for it to topple. Our #reinforcementlearning based motion planning and decision making framework that is being demoed here is tasked with ensuring obstacles avoidance without compromising on the speed, to an extent possible, and to drive the vehicle as fast as possible. This is evident towards the end of the video, where it can be seen that our vehicle avoided static obstacles while drifting. This demonstrates the level of sophistication and agility in our framework to ensure proper control of the #autonomousvehicles at high-speeds. The use cases are many; to begin with, our generic off-roads autonomous driving research focuses on enabling autonomous navigation in previously unknown and unseen environments, while ensuring mathematical completeness guarantees. Such agility can also help on-road autonomous vehicles to deal with unforeseeable corner cases or sudden appearance of obstacles in its tracks, at high-speeds. The underlying research is still far from over, and over the next 3-4 months, we will be demonstrating abstract representation being learned by our multi-RL agents based framework (under progress) to ensure computation of the cost of the terrain without any labelled data, where multiple agents learn to control / regulate different aspects of autonomous navigation, to ensure safe and robust navigation, both on- and off-roads. #deeplearning #machinelearning #robotics

Autonomous driving and obstacles avoidance at drift speeds, challenging the limits of what is possible! In this demo, our vehicle can be seen performing #autonomousdriving at very high speeds, causing it to both skid and drift at turns, while also avoiding obstacles. At such speeds, given the inherent dynamics of the vehicle platform used, it is very easy for the vehicle to topple. The #reinforcementlearning based motion planning and decision making framework that is being demoed here is tasked with ensuring obstacles avoidance without compromising on the speed, to an extent possible, and to drive the vehicle as fast as possible. This is evident towards the end of the video, where it can be seen that our vehicle avoided static obstacles while drifting.This demonstrates the level of sophistication and agility in our framework to ensure proper control of the #autonomousvehicles at high-speeds. The use cases are many; to begin with, our generic off-roads autonomous driving research focuses on enabling autonomous navigation in previously unknown and unseen environments, while ensuring mathematical completeness guarantees. Such agility can also help on-road autonomous vehicles to deal with unforeseeable corner cases or sudden appearance of obstacles in its tracks, at high-speeds. The underlying research is still far from over, and over the next 3-4 months, we will be demonstrating abstract representation being learned by our multi-RL agents based framework (under progress) to ensure computation of the cost of the terrain without any labelled data, where multiple agents learn to control / regulate different aspects of autonomous navigation, to ensure safe and robust navigation, both on- and off-roads. All the people on the ground, who participated in the demo, were trained safety professionals. #deeplearning #machinelearning #robotics Swaayatt Robots (स्वायत्त रोबोट्स)

This demo marks the beginning our generic off-roads #autonomousdriving research, at very high speeds, i.e., near-drift speeds, without requiring HD maps, with driving policies being learned via unsupervised and reinforcement learning. During navigation our vehicle consistently maintained speeds in the range of 50-63 KM/H. For human experts, typical driving speeds range around 45 KM/H, as beyond that there is a high probability of vehicle skidding and drifting (i) around turns, and (ii) when brakes are applied. The terrain was rutted, and highly uneven, with large and sharp pieces of rocks scattered all around. Any error would have resulted in a near-fatal incident for the safety driver (Founder and CEO, Sanjeev Sharma, in this case). At high-speeds, there are high chances of vehicle hitting those pieces of rocks around the road, and getting rattled and thrown off-balance. It can be seem from the intermittent outside camera clips that the vehicle is drifting at many points, but still able to correct its course and controls to gain balance. This demo was done in the city of Bhopal in India. This demo in particular made use of classical #reinforcementlearning for learning control and motion planning policies. The underlying research is our long-shot cutting-edge fundamental R&D, with a goal of allowing #autonomousvehicles to navigate previously unknown and unseen terrains, with no HD maps, for military and general off-roads autonomous navigation applications. One critical component of this research, that makes it different from any off-roads autonomy program out there in the world is that learning is purely unsupervised, with no labelled data required. Going forward, we will showcase driving policies, how they visualize the environment, the learned abstract representation of the world for end-to-end holistic decision making and motion planning, and much more. #deeplearning #unsupervisedlearning #AI

This demo marks the beginning our generic off-roads #autonomousdriving research, at very high speeds, i.e., near-drift speeds, without requiring HD maps, with driving policies being learned via unsupervised and reinforcement learning. During navigation our vehicle consistently maintained speeds in the range of 50-63 KM/H. For human experts, typical driving speeds range around 45 KM/H, as beyond that there is a high probability of vehicle skidding and drifting (i) around turns, and (ii) when brakes are applied. The terrain was rutted, and highly uneven, with large and sharp pieces of rocks scattered all around. Any error would have resulted in a near-fatal incident for the safety driver (myself in this case). At high-speeds, there are high chances of vehicle hitting those pieces of rocks around the road, and getting rattled and thrown off-balance. It can be seem from the intermittent outside camera clips that the vehicle is drifting at many points, but still able to correct its course and controls to gain balance. This demo was done in the city of Bhopal in India. This demo in particular made use of classical #reinforcementlearning for learning control and motion planning policies. The underlying research is a long-shot cutting-edge fundamental R&D program at Swaayatt Robots (स्वायत्त रोबोट्स), with a goal of allowing #autonomousvehicles to navigate previously unknown and unseen terrains, with no HD maps, for military and general off-roads autonomous navigation applications. One critical component of this research, that makes it different from any off-roads autonomy program out there in the world is that learning is purely unsupervised, with no labelled data required. Going forward, we will showcase driving policies, how they visualize the environment, the learned abstract representation of the world for end-to-end holistic decision making and motion planning, and much more. #deeplearning #unsupervisedlearning #AI

Autonomous Driving at scale, through complex geometric terrain and road-network, negotiating stochastic-complex-adversarial traffic-dynamics, on the roads in India. This demo showcases our autonomous vehicle starting from a densely cluttered region, planning its route to the Shree Ram Mandir (its final destination) in the Barkheda region, in the city of Bhopal. Our #autonomousdriving technology enables smooth navigation of our autonomous vehicle through the static and dynamic traffic encountered on the road. This demo utilized our motion planning and decision making algorithmic framework that allows #autonomousvehicles to negotiate bidirectional traffic on single lane roads, that we have been demonstrating since October 2023. The vehicle abides by the traffic rules and remains on the left side of the road when the road is wide enough, and engages open environment navigation when faced with tight single-lane road. The route planning computation happens over our custom GPS maps of the environment. This demo highlights our capabilities to perform sustainable autonomous driving at scale. Next we will be demonstrating end-to-end learned policies via inverse #reinforcementlearning for traffic-intersection and roundabout navigation of autonomous agents in the presence of complex traffic dynamics with stochastic-adversarial agents. We wish everyone a very happy, prosperous, and shubh Diwali!! 🙏 Jai Shree Ram 🙏 #deeplearning #machinelearning #Diwali2024 #India

Autonomous Driving at scale, through complex geometric terrain and road-network, negotiating stochastic-complex-adversarial traffic-dynamics, on the roads in India. This demo showcases Swaayatt Robots (स्वायत्त रोबोट्स) autonomous vehicle starting from a densely cluttered region, planning its route to the Shree Ram Mandir (its final destination) in the Barkheda region in the city of Bhopal. Our #autonomousdriving technology, enables smooth navigation of our autonomous vehicle through the static and dynamic traffic encountered on the road. This demo utilized our motion planning and decision making algorithmic framework that allows #autonomousvehicles to negotiate bidirectional traffic on single lane roads, that we have been demonstrating since October 2023. The vehicle abides by the traffic rules and remains on the left side of the road when the road is wide enough, and engages open environment navigation when faced with tight single-lane road. The route planning computation happens over our custom GPS maps of the environment. This demo highlights our capabilities to perform sustainable autonomous driving at scale. Next we will be demonstrating end-to-end learned policies via inverse #reinforcementlearning for traffic-intersection and roundabout navigation of autonomous agents in the presence of complex traffic dynamics with stochastic-adversarial agents. Wishing everyone in my network a Very Happy and Shubh Diwali! Jai Shree Ram 🙏 Jai Maa Kali 🙏 #deeplearning #machinelearning #computervision

At Swaayatt Robots (स्वायत्त रोबोट्स), we are hiring for multiple positions: - Software Developers (C++ and Python) - Deep Learning Engineers (Off-Road and On-Road Perception / Decision Making Under Uncertainty) - Localization Engineer - Motion Planning Engineer - Android Developer - Deep Learning Engineers (NLP/LLM/Generative AI) - Machine Learning Engineer (expertise in general nonlinear programming, convex optimization, and Bayesian ML) We are also building a small team of machine learning / deep learning engineers, specifically focusing on NLP / LLMs / Generative AI, whose focus would be on solving a class of problems related to autonomous driving, and general robotics. We also hire interns throughout the year, and offer internships as long as 6 months. Those who are interested in deep research, solving challenging problems, and moving the filed of autonomous driving, robotics, and general AI forward can apply at career[at]swaayatt[dot]com #machinelearning #deeplearning #computervision #hiring #reinforcementlearning #autonomousdriving #robotics

As we further our Level-5 autonomous driving research, and scale up the technology, including sustainable last-mile autonomy operational capabilities, we begin our day-time operations on regular urban roads in India. This is to ensure that our #autonomousdriving technology can work globally and can negotiate any kind of environments and traffic-dynamics. We will gradually increase the complexity of the traffic-dynamics and the environments that our #autonomousvehicles are tasked to negotiate. In this demo, the vehicle was tasked with navigating a regular 4-lane city road, with end goal being able to park itself in a very constrained unstructured parking spot. Often, even on 4-lane roads, vehicles, bicyclists, and autos can come from the wrong side. We mimic this stochastic traffic pattern by having motorcyclists come from the wrong side and not yield to our vehicle -- leaving it to our vehicle to decide whether to come to a standstill or to yield from its current driving lane and perform an avoidance maneuver. At the end, we demonstrate last-mile autonomy capability by having our autonomous vehicle park itself in a very unstructured parking spot, with bikes parked randomly in the area, outside of our office. We had earlier demonstrated such capabilities in structured campus environments in our demos in January and February 2023. As can be seen from the demo, vehicle executed these tasks in a generic open environment, while also negotiating a traffic-intersection, and crossing the divider on the 4-lanes road. This is only a beginning of our day-time operations on roads in India. #reinforcementlearning #deeplearning #machinelearning Swaayatt Robots (स्वायत्त रोबोट्स)

Projective 3-Point (P3P) algorithm plays a crucial role in understanding the Visual Odometry and typical SLAM algorithmic pipelines. P3P algorithm allows us to compute camera's (robot's) transformation using the 3D-2D correspondences of feature points in a camera's 2D image plane and their corresponding 3D real-world coordinates. P3P algorithm requires 3 such feature points in an image and their corresponding 3D coordinate. The previous set of lectures discussed the mathematical formulation of the problem statement and step by step solution. The geometry involved, via P3P algorithm, in computation of the camera transformation parameters results in a quartic equation, resulting in 4 possible solutions. This lecture graphically discusses the four possible solutions and how we can use a 4th feature point in an image to get the true transformation. This lecture is a part of my course CV-2.0X: Introduction to Visual Mapping and Odometry at Deep Eigen. We are also providing courses in #reinforcementlearning #computervision #machinelearning and #deeplearning. Upcoming courses also include advanced machine learning with modern deep learning concepts, and mathematical optimization.

On this Independence Day, at Swaayatt Robots (स्वायत्त रोबोट्स) we have taken a pledge of bringing autonomy to all frontiers, i.e., Civil, Military, and Industrial, both on the ground and in the air. Over the years, specifically in the past 9 months, we have demonstrated our #autonomousdriving technology conquering on-roads, off-roads, and geo-fenced autonomous navigation. We are going to hereafter scale it up for multitude of applications. Wishing everyone a very Happy Independence Day! Jai Shree Ram 🙏 Jai Maa Kali 🙏 Har-Har Mahadev 🙏 Jai Hind! 🇮🇳 #autonomousvehicles