Category Robotics Classification

Our FactorySmart® 3D sensors are used to improve production across the gamut of industrial applications, from factory automation to quality inspection and material/process optimization. Booth #4811

Buying a new robot is a considerable investment for any business owner. Collaborative robots have been widely marketed as a cheaper alternative to industrial robots, but what’s the catch?

Deployment of an automation solution that takes into account the process needs, the people as well as the technology, ensures businesses can introduce automation that can augment the workers’ experience whilst also transforming productivity.

In this episode, Lauren Klein interviews Human-Robot Interaction researcher Patrícia Alves-Oliveira. Alves-Oliveira tells us about the upcoming RSS Pioneers workshop at the 2020 Robotics: Science and Systems Conference; the workshop brings senior PhD students and postdoctoral researchers together to collaborate and discuss their work with distinguished members of the robotics field. She also describes her own research designing robots to encourage creativity in children.

Patrícia Alves-Oliveira

Patrícia Alves-Oliveira is an upcoming postdoctoral researcher working with Professor Maya Cakmak in the Human-Centered Robotics Lab at the University of Washington. She recently completed her PhD in Human-Robot Interaction at the Lisbon University Institute supervised by Profs. Ana Paiva and Patrícia Arriaga, also working as a visiting scholar in the Human-Robot Collaboration and Companionship Laboratory at Cornell University, supervised by Professor Guy Hoffman. Patrícia’s research focuses on the use of social robots as intervention tools to enrich creative behaviors in children. Patrícia’s overarching goal as a Human-Robot Interaction researcher is to investigate how and where robots can be used to empower innate human qualities and experiences.

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How Neural Networks can be used in graph data

However, studies in the field of wearable robots are opening up innumerable possibilities of telepresence and telemanipulation that could change the degree and nature of functionality and productivity in humans.

More flexible materials are necessary for safer collaborative work alongside humans and delicate tasks usually performed by humans, such as picking fruit and vegetables or performing minor surgeries.

In this episode, Lilly interviews Juxi Leitner, a Postdoctoral Research Fellow at the Queensland University of Technology and Co-Founder/CEO of LYRO Robotics. LYRO spun out of the 2017 win of the Amazon Robotics Challenge by Team ACRV. Here Juxi discusses deep learning, computer vision, intent in grasping and manipulation, and bridging the gap between abstract and low-level understandings of the world. He also discusses why robotics is really an integration field, the Amazon and other robotics challenges, and what’s important to consider when spinning an idea into a company.

Juxi Leitner

Juxi Leitner is co-founder of LYRO Robotics, a deep-tech startup based in Brisbane, Australia, creating robotic picking and packing solutions. LYRO is a spin-out of the Australian Centre of Excellence for Robotic Vision (ACRV), where Juxi is the research lead for the manipulation research stream (previously Vision and Action project). His research focus is on integrating Robotics, Computer Vision and Machine Learning/Artificial Intelligence (AI) for robust grasping and manipulation in real-world scenarios. In 2017 his team won the Amazon Robotics Challenge. Juxi is active in the local Brisbane deep-tech ecosystem and started Brisbane.AI and the Brisbane robotics interest group.

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Our service robotics modules give you an easy start in robotics. Assemble your individual robot application – exactly according to your requirements: Service robotics modules consists of the manipulator module PRBT, the control module PRCM, the operating module PRTM and ROS modules. Our collision measurement set PRMS for human-robot collaboration (HRC) supports you reliably on your path to a safe application. Application areas of the service robotics modules can be found in service robotics and industrial environments. Benefit from our cost-efficient, flexible solution!

Advanced robotics are already influencing maritime operations. From the green and eco-friendly benefits offered by regular hull cleanings, to the safer waters achieved through anti-piracy measures, the applications are vast.

According to one McKinsey study, the market for industrial robotics has increased by double digits each year since 2012, and will continue this growth at least through 2021.

ATX West, MD&M, Plastic West, West Pack and Design & Manufacturing take place in Anaheim, CA February 11th - 13th. This RoboticsTomorrow.com Special Tradeshow report aims to bring you news, articles and products from this years events.

ATX West will primarily feature the latest in automation and robotic technology including robotic accessories, motion control, and automation software,

Here are some lessons learned while shepherding grocery robots as they’ve logged more than 250,000 miles of pilot runs and hundreds of actual deployments.

By Glen Berseth

All living organisms carve out environmental niches within which they can maintain relative predictability amidst the ever-increasing entropy around them (1), (2). Humans, for example, go to great lengths to shield themselves from surprise — we band together in millions to build cities with homes, supplying water, food, gas, and electricity to control the deterioration of our bodies and living spaces amidst heat and cold, wind and storm. The need to discover and maintain such surprise-free equilibria has driven great resourcefulness and skill in organisms across very diverse natural habitats. Motivated by this, we ask: could the motive of preserving order amidst chaos guide the automatic acquisition of useful behaviors in artificial agents?

How might an agent in an environment acquire complex behaviors and skills with no external supervision? This central problem in artificial intelligence has evoked several candidate solutions, largely focusing on novelty-seeking behaviors (3), (4), (5). In simulated worlds, such as video games, novelty-seeking intrinsic motivation can lead to interesting and meaningful behavior. However, these environments may be fundamentally lacking compared to the real world. In the real world, natural forces and other agents offer bountiful novelty. Instead, the challenge in natural environments is allostasis: discovering behaviors that enable agents to maintain an equilibrium (homeostasis), for example to preserve their bodies, their homes, and avoid predators and hunger. In the example below we shown an example where an agent is experiencing random events due to the changing weather. If the agent learns to build a shelter, in this case a house, the agent will reduce the observed effects from weather.

We formalize homeostasis as an objective for reinforcement learning based on surprise minimization (SMiRL). In entropic and dynamic environments with undesirable forms of novelty, minimizing surprise (i.e., minimizing novelty) causes agents to naturally seek an equilibrium that can be stably maintained.

Here we show an illustration of the agent interaction loop using SMiRL. When the agent observes a state $\mathbf{s}$, it computes the probability of this new state given the belief the agent has $r_{t} \leftarrow p_{\theta_{t-1}}(\textbf{s})$. This belief models the states the agent is most familiar with – i.e., the distribution of states it has seen in the past. Experiencing states that are more familiar will result in higher reward. After the agent experience a new state it updates its belief $p_{\theta_{t-1}}(\textbf{s})$ over states to include the most recent experience. Then, the goal of the action policy $\pi(a|\textbf{s}, \theta_{t})$ is to choose actions that will result in the agent consistently experiencing familiar states. Crucially, the agent understands that its beliefs will change in the future. This means that it has two mechanisms by which to maximize this reward: taking actions to visit familiar states, and taking actions to visit states that will change its beliefs such that future states are more familiar. It is this latter mechanism that results in complex emergent behavior. Below, we visualize a policy trained to play the game of Tetris. On the left the blocks the agent chooses are shown and on the right is a visualization of $p_{\theta_{t}}(\textbf{s})$. We can see how as the episode progresses the belief over possible locations to place blocks tends to favor only the bottom row. This encourages the agent to eliminate blocks to prevent board from filling up.

Left: Tetris. Right: HauntedHouse.

Emergent behavior

The SMiRL agent demonstrates meaningful emergent behaviors in a number of different environments. In the Tetris environment, the agent is able to learn proactive behaviors to eliminate rows and properly play the game. The agent also learns emergent game playing behavior in the VizDoom environment, acquiring an effective policy for dodging the fireballs thrown by the enemies. In both of these environments, stochastic and chaotic events force the SMiRL agent to take a coordinated course of action to avoid unusual states, such as full Tetris boards or fireball explorations.

Left: Doom Hold The Line. Right: Doom Defend The Line.

Biped

In the Cliff environment, the agent learns a policy that greatly reduces the probability of falling off of the cliff by bracing against the ground and stabilize itself at the edge, as shown in the figure below. In the Treadmill environment, SMiRL learns a more complex locomotion behavior, jumping forward to increase the time it stays on the treadmill, as shown in figure below.

Left: Cliff. Right: Treadmill.

Comparison to Intrinsic motivation:

Intrinsic motivation is the idea that behavior is driven by internal reward signals that are task independent. Below, we show plots of the environment-specific rewards over time on Tetris, VizDoomTakeCover, and the humanoid domains. In order to compare SMiRL to more standard intrinsic motivation methods, which seek out states that maximize surprise or novelty, we also evaluated ICM (5) and RND (6). We include an oracle agent that directly optimizes the task reward. On Tetris, after training for $2000$ epochs, SMiRL achieves near perfect play, on par with the oracle reward optimizing agent, with no deaths. ICM seeks novelty by creating more and more distinct patterns of blocks rather than clearing them, leading to deteriorating game scores over time. On VizDoomTakeCover, SmiRL effectively learns to dodge fireballs thrown by the adversaries.

The baseline comparisons for the Cliff and Treadmill environments have a similar outcome. The novelty seeking behavior of ICM causes it to learn a type of irregular behavior that causes the agent to jump off the Cliff and roll around on the Treadmill, maximizing the variety (and quantity) of falls.

SMiRL + Curiosity:

While on the surface, SMiRL minimizes surprise and curiosity approaches like ICM maximize novelty, they are in fact not mutually incompatible. In particular, while ICM maximizes novelty with respect to a learned transition model, SMiRL minimizes surprise with respect to a learned state distribution. We can combine ICM and SMiRL to achieve even better results on the Treadmill environment.

Left: Treadmill+ICM. Right: Pedestal.

Insights:

The key insight utilized by our method is that, in contrast to simple simulated domains, realistic environments exhibit dynamic phenomena that gradually increase entropy over time. An agent that resists this growth in entropy must take active and coordinated actions, thus learning increasingly complex behaviors. This is different from commonly proposed intrinsic exploration methods based on novelty, which instead seek to visit novel states and increase entropy. SMiRL holds promise for a new kind of unsupervised RL method that produces behaviors that are closely tied to the prevailing disruptive forces, adversaries, and other sources of entropy in the environment.

This article was initially published on the BAIR blog, and appears here with the author’s permission.