The　ability　to　implement　semantic　Reach-to-grasp　(RTG)　tasks　successfully　is　a　crucial　skill　for　robots.　Given　unknown　objects　in　an　unstructured　environment,　finding　an　feasible　grasp　configuration　and　generating　a　constraint-satisfied　trajectory　to　reach　it　are　challenging.　In　this　paper,　a　learning　framework　which　combines　semantic　grasp　planning　with　trajectory　generation　is　presented　to　implement　semantic　RTG　tasks.　Firstly,　the　object　of　interest　is　detected　by　using　an　object　detection　model　trained　by　deep　learning.　A　Bayesian-based　search　algorithm　is　proposed　to　find　the　grasp　configuration　with　highest　probability　of　success　from　the　segmented　image　of　the　object　using　a　trained　quality　network.　Secondly,　for　robotic　reaching　movements,　a　model-based　trajectory　generation　method　inspired　by　the　human　internal　model　theory　is　designed　to　generate　a　constraint-satisfied　trajectory.　Finally,　the　presented　framework　is　validated　both　in　comparative　analysis　and　on　real-world　experiments.　Experimental　results　demonstrated　that　the　proposed　learning　framework　enables　the　robots　to　implement　semantic　RTG　tasks　in　unstructured　environments.　©　2018　Elsevier　B.V.