Customer-obsessed science
Research areas
-
July 10, 20265 min readHydroShear, a new physics-based simulator, teaches robots how to use their sense of touch to perform complex manipulation tasks, in a way that transfers seamlessly to the real world. -
July 9, 202610 min read -
-
Featured news
Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features researchers tackling the hardest problems in agentic AI — from building reliable perception systems to designing training environments that mirror human learning.
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
-
ICLR 2026 Workshop on Agents in the Wild2026An AI agent that hallucinates a tool call typically shows no overt signs of failure: the call is syntactically correct, its arguments are well-formed, and the associated token probabilities appear routine. In these cases, the execution pipeline receives no indication that anything is amiss. The call executes, downstream components consume its output, and the erroneous transaction completes without human
-
IROS 20262026Existing robotic foundation policies are trained primarily via large-scale imitation learning. While such models demonstrate strong capabilities, they often struggle with long-horizon tasks due to distribution shift and error accumulation.While reinforcement learning (RL) can finetune these models, it cannot work well across diverse tasks without manual reward engineering. We propose VLLR, a dense reward
-
UAI 20262026The problem of relevant and diverse subset selection has a wide range of applications, from recommender systems to retrieval-augmented generation(RAG). For example, in recommender systems, one is interested in selecting relevant items, while providing a diversified recommendation. Constrained subset selection problem is NP-hard, and popular approaches such as Maximum Marginal Relevance (MMR) are based on
-
Knowledge-Based Systems Journal2026Knowledge graphs provide a source of up-to-date structured knowledge, which makes them an ideal counterpart to LLMs. LLMs, by themselves, are not trained to run structured queries internally and can become stale without a source of up-to-date information. We hypothesize that knowledge graphs can be effectively connected to large language models via controlled natural languages. Unlike standard formal query
-
ICML 2026 Workshop on Generative and Agentic AI for Biology2026Protein design requires extrapolating beyond training data to achieve higher fitness. State-of-the-art methods typically fine-tune billion-parameter language models end-to-end, often combined with external scorers, data distillation, and multiple rounds of iterative refinement. We introduce a residual latent adapter, a 5M parameter MLP inserted between the encoder and decoder of a frozen ProtT5-3B model
Collaborations
View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
View all
Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
Courtesy of Pai-Ling Yin
We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
