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Efficient Robotic Packing: MIT’s Breakthrough with AI

Anyone who has tried to pack family-sized luggage into a sedan-sized trunk knows this is a hard problem. Robots need help with dense packing tasks, too. For the robot, solving the packing problem involves satisfying many constraints, such as stacking luggage so suitcases don’t topple out of the trunk, heavy objects aren’t placed on top of lighter ones, and collisions between the robotic arm and the car’s bumper are avoided.

Some traditional methods tackle this problem sequentially, guessing a partial solution that meets one constraint at a time and then checking to see if any other constraints were violated. This process can be impractically time-consuming, with a long sequence of actions and a pile of luggage to pack.

Packing tasks are time-consuming due to their sequential nature, especially when it involves fitting luggage into a small space such as a car’s trunk. Robots have their unique set of challenges when dealing with such tasks.

MIT researchers employed a diffusion model, a type of generative AI, to address this challenge with increased efficiency. Their approach involves an ensemble of machine-learning models, each specialised in representing distinct constraints. By integrating these models, they can produce comprehensive solutions for the packing problem, considering all constraints simultaneously.

Their approach proved to be faster and more productive in generating solutions and exhibited adaptability by handling novel constraint combinations and larger object sets beyond their training scope.

This generalisability opens the door to instructing robots in comprehending and adhering to various packing constraints. For instance, it can teach them the significance of collision avoidance or the arrangement of objects in proximity. Such trained robots can be applied across diverse domains, ranging from efficient warehouse order fulfilment to organising household bookshelves, extending their capabilities in unstructured human environments.

Zhutian Yang, the paper’s lead author detailing this innovative machine-learning technique, envisioned the potential of advancing robots to undertake more intricate tasks characterised by numerous geometric constraints and intricate decision-making processes. With the versatile tool of compositional diffusion models, they can now tackle these complex challenges while achieving generalisation results.

Continuous constraint satisfaction problems pose unique challenges for robots. They arise in complex multi-step tasks, like packing items into a container or setting a table, where various constraints must be met. These constraints encompass geometric aspects, such as ensuring the robot arm doesn’t collide with its surroundings. Physical considerations like arranging objects for stability and qualitative requirements, for instance, positioning a spoon to the right of a knife.

The number and nature of these constraints can vary widely across tasks and environments, contingent on factors such as object geometry and specific human-defined criteria.

MIT researchers devised a machine-learning approach known as Diffusion-CCSP to address these challenges effectively. Diffusion models are trained to enhance their output iteratively, creating new data samples resembling those in a training dataset. They achieve this by learning a process for incremental improvements to a potential solution. In solving a problem, they begin with a random, often suboptimal solution, progressively refining it over time.

This method is well-suited for tackling continuous constraint-satisfaction problems because it allows multiple models to collectively influence an object’s pose, promoting all constraints’ satisfaction. The models can produce a diverse array of solutions by initiating the process with a random initial guess in each iteration.

The Diffusion-CCSP approach addresses complex constraint satisfaction problems by considering the interdependencies of constraints, such as those encountered in packing tasks. It employs a family of diffusion models dedicated to specific constraint types. These models are collectively trained, sharing knowledge like object geometries. By working in concert, they identify solutions that satisfy multiple constraints simultaneously.

The method iteratively refines solutions, learning from violations to achieve better results. Notably, it reduces the training data needed compared to other methods despite requiring substantial data. The team generated simulation solutions and demonstrated their technique with a real robot, consistently outperforming other methods. Future applications may involve more complex scenarios and broader domains without retraining.

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Qlik’s vision is a data-literate world, where everyone can use data and analytics to improve decision-making and solve their most challenging problems. A private company, Qlik offers real-time data integration and analytics solutions, powered by Qlik Cloud, to close the gaps between data, insights and action. By transforming data into Active Intelligence, businesses can drive better decisions, improve revenue and profitability, and optimize customer relationships. Qlik serves more than 38,000 active customers in over 100 countries.

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CTC Global Singapore, a premier end-to-end IT solutions provider, is a fully owned subsidiary of ITOCHU Techno-Solutions Corporation (CTC) and ITOCHU Corporation.

Since 1972, CTC has established itself as one of the country’s top IT solutions providers. With 50 years of experience, headed by an experienced management team and staffed by over 200 qualified IT professionals, we support organizations with integrated IT solutions expertise in Autonomous IT, Cyber Security, Digital Transformation, Enterprise Cloud Infrastructure, Workplace Modernization and Professional Services.

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Planview has one mission: to build the future of connected work. Our solutions enable organizations to connect the business from ideas to impact, empowering companies to accelerate the achievement of what matters most. Planview’s full spectrum of Portfolio Management and Work Management solutions creates an organizational focus on the strategic outcomes that matter and empowers teams to deliver their best work, no matter how they work. The comprehensive Planview platform and enterprise success model enables customers to deliver innovative, competitive products, services, and customer experiences. Headquartered in Austin, Texas, with locations around the world, Planview has more than 1,300 employees supporting 4,500 customers and 2.6 million users worldwide. For more information, visit www.planview.com.

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SIRIM is a premier industrial research and technology organisation in Malaysia, wholly-owned by the Minister​ of Finance Incorporated. With over forty years of experience and expertise, SIRIM is mandated as the machinery for research and technology development, and the national champion of quality. SIRIM has always played a major role in the development of the country’s private sector. By tapping into our expertise and knowledge base, we focus on developing new technologies and improvements in the manufacturing, technology and services sectors. We nurture Small Medium Enterprises (SME) growth with solutions for technology penetration and upgrading, making it an ideal technology partner for SMEs.

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HashiCorp provides infrastructure automation software for multi-cloud environments, enabling enterprises to unlock a common cloud operating model to provision, secure, connect, and run any application on any infrastructure. HashiCorp tools allow organizations to deliver applications faster by helping enterprises transition from manual processes and ITIL practices to self-service automation and DevOps practices. 

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IBM is a leading global hybrid cloud and AI, and business services provider. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,000 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM’s hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently and securely. IBM’s breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM’s legendary commitment to trust, transparency, responsibility, inclusivity and service.

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